MR Hybrid Imaging into Radiation Therapy Treatment

I n t e g r a t i o n of PE T / M R Hybrid Imaging i n t o R a d i a t i o n Th e r a p y Treatment Tong Zhu, PhDa, Shiva Das, PhDa, Terence Z. Wong, MD, PhDb,* KEYWORDS  Hybrid PET/MR imaging  Radiation therapy  PET tracers  Tumor response  Normal tissue toxicity  Tumor heterogeneity  MRI-based treatment planning

KEY POINTS  Hybrid PET/MR imaging is still in early development for treatment planning.  The high initial investment and maintenance costs raise questions of whether PET/MR imaging for radiation therapy treatment planning is superior to that of PET/computed tomography.  There are ongoing improvements in PET/MR imaging workflow, more specific PET tracers, and fast and robust MR imaging acquisition protocols.  PET/MR imaging will play an increasingly important role in better target delineation for treatment planning.  PET/MR imaging will have clear advantages in early evaluation of tumor response and in better understanding of tumor heterogeneity.

Radiation therapy (RT) is an important part of the standard of care for most solid tumor types. The principle of modern RT is to deliver a tumorkilling level radiation dose precisely and accurately to the targeted volume, normally solid tumors as well as surrounding regions with high-risk of microscopic invasions, while minimizing dose to critical normal tissue structures nearby. This delivery can be achieved using high energy photon, electron, or heavily charged particle beams. Following radiobiology principles, the prescribed lethal dose is delivered in a fractionated fashion in most cases, such as 2 Gy per fraction, to balance between killing tumor cells and normal cell repair.

RT relies on modern medical imaging techniques to achieve precise localization and dose delivery. Computed tomography (CT) imaging is currently the most important imaging modality that has been an integral part of each step of a typical RT treatment (Fig. 1). CT scans are fast and widely available in clinical practice. CT images provide high-resolution and distortion-free anatomic information of human body, which are critical for high precision and accuracy required by RT. More important, there is an approximate linear correlation between the voxel intensity within CT images (often referred as the CT number) and the electron density of the soft tissue within image voxels, enabling the attenuation of various tissues to be calculated. Therefore, CT images

The authors have nothing to disclose. a Department of Radiation Oncology, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27599, USA; b Department of Radiology, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27599, USA * Corresponding author. E-mail address: [email protected] Magn Reson Imaging Clin N Am 25 (2017) 377–430 http://dx.doi.org/10.1016/j.mric.2017.01.001 1064-9689/17/Ó 2017 Elsevier Inc. All rights reserved.

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INTRODUCTION

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Fig. 1. A typical procedure of radiation therapy treatment. RECIST, Response Evaluation Criteria in Solid Tumors; WHO, World Health Organization. (Modified from oral presentation of Dr. Brian Ross, Department of Radiology, University of Michigan, Ann Arbor, MI.)

can be used readily for estimating the radiation dose to tissues in vivo for treatment planning.

Radiation Therapy: From Simulation to Treatment Delivery Simulation A typical RT treatment (see Fig. 1) starts with an initial medical consultation and a special image acquisition called simulation, during which high-resolution anatomic CT images of the tumor region or the tumor bed in case of postsurgical radiotherapy are acquired. There are several important distinctions between simulation CT scans and the conventional diagnostic CT scans. Patients are scanned in body positions corresponding with the actual treatment position. This treatment position is maintained for the simulation and the subsequent RT treatments. A flat, hard table top equipped with dedicated immobilization devices designed for different body regions minimizes bulk body motion, and is always used for CT simulation. The overall aim of these special setups during simulation imaging is to minimize potential inconsistencies of the relative spatial location of tumor targets and the surrounding normal tissues between the time of CT simulation and of actual treatments so that the lethal dose can be delivered precisely to the tumor region. Contouring Based on the anatomic CT images acquired during simulation, radiation oncologists delineate (contour) the tumor region as well as critical normal structures where the radiation dose needs to be limited to reduce normal tissue toxicity. To improve the robustness, most clinics follow a 3tier definition system recommended by Technical Report 50 from the International Commission on Radiation Units and Measurement1 to guide the

definition of target volumes. These recommendations suggest physician-contoured gross tumor volume (GTV) and clinical target volume (CTV) to represent the target volume before treatment planning. During treatment planning, a planning target volume (PTV) created from the CTV as well as volume contours for critical normal tissues nearby, also referred the organ at risk (OAR), are also recommended. A schematic presentation of these volumes as well as real examples of them from a patient with nasopharyngeal carcinoma are shown in Fig. 2. The GTV often consists of palpable or visible malignancy from medical images. This volume ideally represents the maximum concentration of tumor cells in vivo. A margin is then created from the GTV to generate the CTV to represent subclinical involvement in tissues adjacent to the GTV, such as individual malignant clusters and microscopic extension tumor cells. Suspected malignant cells, such as regional lymph nodes, are also considered as parts of the CTV. In postsurgical cases, only the CTV is created. Owing to the fractionated nature of RT treatment, the planned dose is delivered across a period of several days to several weeks. Consequently, there are inevitable variations contributed by patient position owing to setup errors, by changes in CTV shape and size owing to different filling of surrounding tissues (such as rectal filling), and by small variations in performances of treatment machines (such as field sizes). A volume extension is then created from the CTV as the PTV to accommodate the variations and ensure all tissues within the CTV receive the prescribed dose. Normal tissue toxicity is an essential dose-limiting factor for RT. It is, therefore, important for contouring OARs, especially when there is complex organ geometry near the PTV. For example, in Fig. 2B, the

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 2. Target volume definition and radiation dose distribution in radiation therapy (A). Target volume definition according to ICRU report 50, (B) examples of target volumes for a patient with head and neck cancer, (C) dose distribution from treatment planning is superimposed on the simulation CT and illustrated in the color-wash format, (D) the cumulative dose volume histograms quantify dose to target volumes and organs at risk. GTV, gross tumor volume; ICRU, International Commission on Radiation Units and Measurements; OAR, organ at risk.

CTV (the purple contour) is very close to the brainstem (the cyan contour). To avoid high dose to the brainstem, the extension from the CTV to the PTV (the blue contour) was intentionally reduced toward the brainstem. There are several sources for uncertainty in contouring, most notably poor soft tissue contrast from CT images and interrater and intrarater variabilities. Contouring is arguably the greatest challenge in modern RT. In contrast with poor soft tissue contrast in CT, MR imaging provides superior soft tissue contrast and flexibility of image acquisition in any orientation. Conventional anatomic MR imaging, most notably T1-weighted with and without gadolinium contrast, T2weighted and fluid-attenuated inverse recovery T1- and T2-weighted images, have been used routinely in assistance of target and OAR contouring after image registration with simulation CT images. A major disadvantage of MR imaging for radiation treatment planning is that, unlike CT, the information on electron density required to calculate treatment dose is not provided directly. Clinical studies have shown that incorporation of MR imaging with CT improves the target delineation in most of body sites treated with RT (Fig. 3), including the brain, head and neck, breast, and pelvis.2 Treatment planning Using CT data as the virtual patient, the team of radiation oncologists, dosimetrists, and medical physicists can now decide how to arrange external

radiation spatially so that the resulting dose distribution calculated on the virtual patient has the prescription dose concentrated at the PTV while minimizing the dose to normal tissues. This process is called treatment planning and is now mainly performed with dedicated computer software, often referred as the treatment planning system. Advances in computer science and computer-driven multiple leaf collimator in early 1990s are the main driving forces for treatment planning. Using CT data, the treatment planning system visualizes the projection of the PTV and OARs along selected radiation beam incident angles and, using a multiple leaf collimator, the radiation aperture is shaped to conform to the PTV projection. The dose distribution from the selected beam arrangement can be superimposed onto the simulation CT for visualization (see Fig. 2C) and, more important, dose to the PTV and OARs can be quantified by, for example, the cumulative dose volume histogram (see Fig. 2D), which is a 2-dimensional plot of the histogram of the 3dimensional (3D) dose within the structure. Cumulative dose volume histogram curves have dose along the horizontal axis and percentage volume receiving greater than the dose on the vertical axis. Using the dose volume histogram and dose distribution on the CT image, the planning team can adjust the beam arrangements manually until a satisfactory and optimized dose distribution is achieved. The plans generated with the abovementioned process are referred as the 3D conformal radiotherapy (3DCRT) plans. Although

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Fig. 3. Anatomic sites with potential improvement in target delineation from MR imaging, including (A) head and neck cancer, (B) prostate cancer, (C) gynecologic cancer, (D) rectal cancer, and (E) breast cancer. The left images are the simulation computed tomography (CT) images. The middle images are the corresponding slices from coregistered MR images. The right images display the transferred MR imaging-defined contours superimposed onto the simulation CT. (From Devic S. MRI simulation for radiotherapy treatment planning. Med Phys 2012;39:6704; with permission.)

3DCRT is capable of delivering a uniform dose to the PTV, it cannot generate a complicated dose distribution for tumor regions with concave PTVs that are immediately adjacent to critical normal tissues. For example, in treating nasopharyngeal cancer, the brainstem, whose dose tolerance is 54 Gy, is sometimes adjacent to the PTVs with a prescription dose of up to 70 Gy (see Fig. 2). Intensity-modulated RT (IMRT) and volumetricmodulated arc therapy (VMAT) were developed to

address the limitations of 3DCRT. Instead of manual iterative adjustments in 3DCRT, IMRT/ VMAT takes user input goals for the PTV and normal tissues and uses an automatic algorithm to generate a sequence of aperture shapes and dose outputs through these apertures to attempt to satisfy the user’s goals (Fig. 4B). Compared with 3DCRT, IMRT/VMAT creates sharp dose falloff gradient from the high-dose region in PTVs to low dose in adjacent OARs in a short physical

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 4. While treating planning target volume (PTV; pink contours), standard 3-dimensional (3D) 4-field plan (A1 and A2) delivers full prescription dose to small bowel (green contours), which can be spared with intensitymodulated radiation therapy (IMRT; B1 and B2). Thin color lines represent different radiation dose levels, often referred as isodose lines. After a hysterectomy, the small bowel can settle into the pelvis where the uterus was previously located. As a result, it received the therapeutic prescription dose (A) and consequently, a significant number (50%–90%) of patients undergoing standard 4-field 3D chemoradiotherapy (CRT) reported grade II or higher acute gastrointestinal toxicity. Compared with a uniform dose distribution (ie, uniform intensity of the fluence map) within each field of the 3D CRT plan (A2), combinations of multiple leaf collimator (MLC) segments of the IMRT plan (B2) intentionally create nonuniform intensity within the fields to achieve the desired dose distribution for complex anatomy around PTV and surrounding organs at risk. Small horizontal bars on A2 and B2 are individual leaves of MLC, which can be controlled by computers to form desired field shapes according to the treatment planning software. (Courtesy of Heather Baliker and Jackie Carter, University of North Carolina.)

distance, even for complicated PTV-OARs geometry such as panpelvic radiation of lymph nodes for postoperative cervical cancer treatment (see Fig. 4). A critical factor for successful IMRT planning is to have detailed and accurate knowledge of PTVs and OARs so that the inverse planning algorithm can derive an accurate dose distribution. Treatment delivery Based on radiobiological principles, RT is normally delivered in multiple fractions (as many as 35). Before each fraction, patient position is verified against the simulation CT using portal radiographic imaging or on-board cone beam CT if critical OARs are adjacent to the PTVs. This can account for day-to-day variations in positioning and organ shifts. Treatment response evaluation Imaging also plays an essential role in evaluation of treatment response (see Fig. 1). Posttreatment imaging is normally preformed at 2 to 3 months after RT is completed. Currently, the most commonly used evaluation criteria for solid tumors is imaging-guided tumor size measurement following the guidelines first proposed by the World Health Organization in 19813 and further revised as the

Response Evaluation Criteria in Solid Tumors (RECIST) in 20004 and as RECIST 1.1 in 2009.5 In brief, RECIST standardizes target lesion size measurement at the pretreatment imaging (as the baseline) and each follow-up imaging as the sum of their unidimensional size measurements. With comparisons to the baseline, patients are categorized into 4 response groups of complete response, partial response, stable disease, or progressive disease, solely based on target lesion size measured at each follow-up. Clinical decisions are then made mainly based on different response categories. For example, for the partial response and stable disease groups, potential boost radiation treatment will be carried and, in contrast, different treatment modalities or salvage radiation treatment will be the likely choice for the progressive disease group (see Fig. 1). However, several limitations of RECIST have been well-documented.6,7 One fundamental limitation is that RECIST reflects a simplified tumor size measure. Volumetric change in tumor results from a cascade of complicated and progressive pathophysiologic and biological responses to radiation, which normally occurs weeks and months before any detectable anatomic changes.8 In addition, many new treatment techniques may be

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From Anatomic Imaging to Functional Imaging Given the limitations of conventional anatomic imaging, there is a great interest in functional imaging modalities that can provide information on tumor radiobiology, such as angiogenesis and hypoxia, and pathophysiologic processes, including abnormal metabolism and uncontrolled proliferation, both spatially and temporally. These image features, often referred as image biomarkers, should be predictive to guide and optimize treatment strategy. For example, if early functional imaging examination during the treatment detects hypoxic subvolumes, a switch to a more effective treatment, such as simultaneous dose escalation11 to radiation-resistant hypoxic subvolumes with IMRT techniques, or concurrent prescription of drugs that specifically target hypoxic cells,12,13 can be carried out to improve treatment efficacy. For normal tissue toxicity, such functional imaging modalities can detect spatial location and severity of early onset radiationinduced injuries so that early pharmacologic therapies or reoptimization of the treatment plan can take place to prevent or slow down the sequence of biological events that will, if not intervened, lead to permanent radiation-induced injuries.14 Molecular imaging modalities, such as PET, provide promising solutions to address these clinical issues. The strength of PET is its high sensitivity, requiring only a very small amount of radiolabeled tracer (picomolar range) injected in vivo. An increasing number of PET tracers are specific to many biological processes that are the hallmarks of cancer cells.15 For example, uptake of 18F-2deoxy-D-glucose (18F-FDG) increases with upregulating glucose transporters owing to abnormal metabolism of cancer cells and sustained proliferative signal,15 18F-fluroromisonidazole (18FMISO) are sensitive and specific to hypoxic tumor cell clusters owing to abnormal perfusion and tumor vasculatures from disordered angiogenesis. Uptake of 18F-fluoro-3’-deoxythymidine (18F-FLT) increases in tumor cells owing to accelerated synthesis of DNA and increased thymidine kinase 1 in tumor,16 an imaging marker for uncontrolled proliferation.

In parallel to advances in clinical PET applications in RT, recent developments in MR imaging, especially parallel imaging17–19 and parallel RF transmission20 that improve imaging acquisition efficiency and reduce image artifacts owing to geometric distortion, have made several advanced MR imaging techniques applicable to clinical RT applications. In contrast with conventional anatomic MR imaging, that is, T1, T2, and proton density MR imaging, contrasts in these MR imaging modalities directly or indirectly correlate with the physiologic and biological processes closely associated with tumor biology and radiobiology. For example, quantitative measures derived from dynamic contrast-enhanced MR imaging (DCE-MR imaging) quantify changes in microvascular permeability owing to abnormal angiogenesis in tumor. Changes in oxygen concentration in venous vessels and in extracellular and extravascular space owing to hypoxic condition will result in detectable changes of image intensity of blood oxygenation level– dependent (BOLD) and tissue oxygenation level–dependent (TOLD) MR imaging, respectively. Changes in cell density owing to abnormal proliferation rate in tumor and break-down of cell membranes under radiation will affect microscopic water diffusion in vivo, which will be detected by diffusion-weighted MR imaging (DWI) and diffusion tensor MR imaging (DTI). With technical advances in both PET and MR imaging ends, the advantages of having a hybrid PET/MR imaging became appealing for RT. (1) Compared with PET/CT, PET/MR imaging provides high-resolution anatomic MR images with superior soft tissue contrast, which may further reduce variations in target delineation. (2) Functional information from MR imaging and PET are complementary and simultaneous acquisitions of both modalities will potentially compensate modeling/technical limitations of each modality and consequently provide better understanding of complexity in tumor pathology and radiobiology. For example, the physiologic causes for increased uptake of 18F-FDG-PET in tumor cells are multifactorial, including increased metabolism level and local abnormal perfusion conditions near and inside the tumor. With simultaneous acquisition of DCE-MR imaging and 18F-FDGPET imaging, it is possible to study the correlation between dynamic changes of tracer uptake and local perfusion dynamics. Information from DCEMR imaging can be incorporated into the PET tracer kinetic model to improve its overall robustness. 18FMISO-PET has been an important imaging biomarker for hypoxia. However, with a coarse voxel resolution (commonly a scale of 4 mm to 1 cm), partial volume effects in PET will

PET/MR Hybrid Imaging into Radiation Therapy potentially include both hypoxic and nonhypoxic clusters (Fig. 5C). In contrast, TOLD MR imaging has much better image resolution (see Fig. 5B) and, therefore, can be used to correct partial volume effect in PET data. (3) With simultaneous acquisitions of multiple functional parameters of tumors, metabolism, and hypoxia, perfusion and diffusion maps from PET/MR imaging are naturally fused together with minimal misregistration spatially. For the first time, it may become possible to perform robust multiparametric image analysis to address patient-specific tumor features, such as intratumor heterogeneity. Consequently, these new results can steer RT treatment strategy toward personalized radiotherapy with aids of more accurate delineation of biological target volume21 and advanced treatment delivery techniques such as IMRT/VMAT. (4) Last, an integrated PET/MR imaging also has several practical advantages. It can provide “one-stop” acquisitions for all PET and MR imaging images necessary for diagnostic and treatment planning purpose instead of separate stops for PET and MR imaging acquisitions in current practice. Minimized radiation exposure with PET/MR imaging is especially important for pediatric patients and young adults.

PET/MR IMAGING FOR HALLMARKS OF CANCER RADIOBIOLOGY IN RADIATION THERAPY Compared with PET/CT, previous comparative studies22–24 demonstrated that the combination of PET and anatomic MR imaging can achieve a more accurate delineation of the gross target volumes and better identification of normal tissue boundaries for RT treatment planning, especially for brain tumors, recurrent breast cancer, and prostate and bone metastases. In addition, PET/MR imaging with integration of multiparametric MR imaging beyond anatomic MR imaging, such as DWI, perfusion MR imaging and MR spectroscopy, will further improve sensitivity and specificity of tumor detection and target delineation, for example, multiparametric MR imaging has been recommended as the imaging standard for detecting and staging tumors inside the prostate gland.25,26 More important, the synergy of complementary information from simultaneous PET and MR imaging creates unprecedented opportunities to quantify, both spatially and temporally, pathophysiologic processes that are hallmarks of tumor response to RT. With these quantitative measures, effective early assessment of tumor response or dynamic

Fig. 5. Effect of image resolution of hypoxia imaging modalities of PET and MR imaging. (A), histologic section of a fibrosarcoma grown in a Fischer-344 rat. The tumor is approximately 16 to 12 mm in cross-section. Orange staining regions indicate EF5, a hypoxia marking drug, binding in hypoxic regions. (B) Each small red box represents 1 mm2 spatial resolution, a typical achievable MR imaging resolution (top). The bottom section discretizes intensity of EF5 staining in each box in grayscale. The most intensely stained regions are white, nonstaining regions are in black, and intermediate regions are in gray. With this spatial resolution, the hypoxic band is still clearly visible. (C) The red box represents 1 cm2 spatial resolution of typical PET images (top). (Bottom) Intensity of EF5 staining on the same grayscale as in B. Here the color is coded as gray, because the voxel contains a mixture of hypoxic and nonhypoxic regions. These partial volume effects obscure the presence of the hypoxic band and instead report the voxel as representing an intermediate level of hypoxia. (From Dewhirst MW, Birer SR. Oxygen-enhanced MRI is a major advance in tumor hypoxia imaging. Cancer Res 2016;76:770; with permission.)

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Zhu et al monitoring of normal tissue toxicity become possible, which will potentially lead to paradigm shift changes in RT treatment strategy for more effective tumor control and minimized radiation injuries to normal tissue. Hypoxia, accelerated proliferation of tumor cells, and intrinsic radioresistance of cancer stem cells are identified as 3 main mechanisms causing failures of RT.27 Many key and interrelated biological processes of each mechanism, such as angiogenesis, perfusion, increased metabolism and hypercellularity of tumor cells, can be quantified by PET and MR imaging.

PET/MR Imaging for Metabolism and Proliferation The 2 most fundamental hallmarks of cancer cell are its capability of deregulating the growth promotion signals and suppress the antiproliferation signals. These are what Hanahan and Weinberg called “sustaining proliferative signaling” and “evading growth suppressors”15 to maintain the chronic cell proliferation, a so-called “reprogramming energy metabolism,” another hallmark of cancer cell biology,15 emerges to adjust energy metabolism. Cancer cells prefer aerobic glycolysis, during which the pyruvate is converted into lactate instead of going through the tricarboxylic acid cycle and oxidative phosphorylation process. Many biological processes associated with tumor metabolism and proliferation can be imaged by specifically designed PET tracers or functional MR imaging. A short summary of imaging principles of these modalities as well as their advantage/disadvantages is provided in Table 1. Definition of target volume for radiation treatment planning is one of the most challenging components for modern RT, especially with highly conformal beam delivery techniques, such as IMRT. Integration of functional information from PET, such as increased metabolism from 18F-FDG-PET, and from MR imaging, such as increased cellular density owing to aberrant cell proliferation from DWI, greatly improved the accuracy of target volume delineation. Several comparative studies have shown that, compared with a PTV definition based on CT only, integration of 18F-FDG-PET has decreased interrater variability28 and improved the accuracy of target definition for a variety of cancer treatments.29 In many institutions, 18F-FDG-PET/CT scanning has been used as the standard approach for target definition of lung,30,31 cervical,32,33 and head and neck cancers.34 Decreased signal in apparent diffusion coefficient (ADC) maps from DWI has been used as imaging biomarker for grading and volume delineation of glioma35 and

cervix.36 Multiparametric MR imaging, including DWI and DCE-MR imaging, has been increasingly applied for radiation treatment planning of prostate cancer.37,38 With advances in pulse sequence design and fast imaging techniques, whole-body DWI plays more and more important role in oncologic applications, including screening and detection of remote metastases.39 Several recent developments in novel PET tracers and MR imaging techniques also open new opportunities of improving sensitivity and specificity of PET and MR imaging for the characterization of tumor metabolism and proliferation. A short summary of these developments is provided in this section. Among them, application of DWI with high b values (>2000 s/mm2) may have immediate impact on clinical management and radiation treatment of many solid tumor types. Diffusion is very sensitive to cell density, intracellular and extracellular volumes, which can be used for the detection of hypercellularity in tumors owing to aberrant proliferation and for early evaluation of tumor response. For most solid tumors being treated by RT, water diffusion is approximately isotropic. In body temperature, free water diffusion in vivo is about 3  10 6 mm2/ms. With a typical b value of 1000 s/mm2, clinical DWI will measure the displacement of free water molecules of 30 microns. Simplified illustrations of DWI applications for RT are included in Fig. 6. In an image voxel of normal brain white matter tissues (see Fig. 6A), intracellular and extracellular diffusion (represented by yellow and red dots, respectively) do not exchange owing to myelin sheath and both contribute to measured diffusion in DWI. In tumor tissue, cells are highly packed with a slightly increased cell size (see Fig. 6B). Hypercellularity reduces extracellular diffusion dramatically and increases intracellular diffusion mildly. It leads to an overall decrease in diffusion. After RT, radiation-induced injury, such as necrosis, may damage/destroy intracellular organelles and cell membrane, which will open gates for free diffusion between extracellular and intracellular space (see Fig. 6C). Overall diffusion increases. Using a high b value, all tissues with fast diffusion will attenuate to low signal, except tumor cells, which are highlighted owing to hypercellularity and restricted diffusion (see Fig. 6D). A recent study of high b value DWI for radiation target volume delineation of high-grade glioblastoma by Pramanik and colleagues40 is one of the examples. Standard MR imaging includes Gd contrast-enhanced T1 and fluid-attenuated inversion recovery T2 MR imaging. DWI images with a b value of 1000 s/mm2 are often included for assistance of tumor grading. Owing to tumor

Table 1 PET/MR imaging techniques for metabolism and proliferation in radiation therapy Imaging Techniques

Biological Processes

Quantitative Measures

Pathophysiologic Correlations

1. Glucose is an essential SUVmax/TBR/TMR nutrition for cell metabolism. 2. 18F-FDG is a glucose derivative and taken in through glucose transporters and be phosphorylated by hexokinase.

Accumulation of F-FDG within cells can be used as a surrogate biomarker for glucose uptake to quantify metabolic activity and viability of tumor cells.

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F-FLT

1. A derivative of thymidine that is a build block for DNA synthesis during the S phase. 2. Cells take in FLT through nucleoside transporter on membranes. FLT will be phosphorylated by TK1.

The target molecule for FLT 1. Indirectly reflect cell is TK1, which can be proliferation. overexpressed, up to 15 2. Highly specific to the times, at the S phase of change in proliferation tumor cells compared rate without being with normal cells. Its affected by false positive concentration is owing to inflammation. correlated to the TK1 3. Used mainly as a specific activity. marker for early assessment for RT.

Disadvantages

1. Directly involves cell 1. Low spatial resolution metabolism. (4 mm and above). 2. High sensitivity at the pi- 2. Increased uptake in recomolar level. gions with radiation3. Relatively rapid clearance. induced inflammation. 4. Relatively long half-life. 3. Not for brain owing to 5. Easy to be commercialized high metabolism in and be transported. normal tissue and for prostate owing to slow growth rate. 1. Low spatial resolution (4 mm and above). 2. Owing to overall short period of the S phase in the whole cell cycle, overall uptake of FLT within tumors is much less compared with the uptake of 18F-FDG. Used mainly for evaluation of response instead of initial staging. (continued on next page)

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F-FDG

SUVmax/TBR/TMR

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Table 1 (continued )

Imaging Techniques

Biological Processes

Quantitative Measures

Pathophysiologic Correlations

MET/FET and 11C-, 18 F-choline

Uncontrolled tumor cell SUVmax/TBR/TMR proliferation leads to accelerated synthesis of proteins and synthesis of membrane lipid. This group of PET tracers are building blocks of either protein or membrane lipid.

1. The intracellular uptake of MET correlate with the activity of amino acid transporters. 2. 11C-choline can be used for the production of phospatidylcholine, which is the primary building block for membrane lipid.

68

PSMA is a transmembrane SUVmax/TBR/TMR protein specific to prostate. 1. Overexpress, >100fold, on cell membrane of nearly all prostate carcinoma. 2. Expression increased with stages and aggressiveness.

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Ga-PSMA

Advantages

Disadvantages

MET is mainly for cerebral gliomas 1. Low in normal gray matter but increase in gliomas and not in inflammatory regions. 2. Can cross blood–brain barrier, better than FLT for low-grade glioma Choline is mainly for prostate cancer, especially for biochemical recurrence when PSA is low

1. Low spatial resolution (4 mm and above). 2. Short half-life time for 11 C, around 20 min, which limits its broad use in clinical applications. 3. FET has a longer half life time but low TBR. 4. High uptake of 11C-/18Fcholine was also observed in inflammatory lesions.

Ga-PSMA is 68Ga-labeled 1. High sensitivity and speci- 1. Low spatial resolution small molecules of PSMA ficity, high accumulation (4 mm and above). inhibitors or antibody, even in small tumors. 2. Half-life is about 68 min, which can bind with 2. Clearance of the tracer which requires on-site PSMA. Most of these from nontarget tissues is generation. tracers will have quick, which leads to a 3. 68Ga is produced by 68 Ge/68Ga generator, not glutamate and also a good TBR. cyclotron, the limited zinc-binding component. availability of 68Ge/68Ga generators restrains its clinical applications.

Water diffusion in vivo is Apparent diffusion Very sensitive to cell hindered by cell coefficient/ density, intracellular and membranes, fractional extracellular volume, intracellular and anisotropy/mean which can be used for extracellular organelles diffusivity detection of and microstructures; hypercellularity in therefore, reflects the tumors owing to spatial organization of aberrant proliferation, surrounding anatomic for early evaluation of structures. tumor response.

MRS

Many metabolites in vivo Area under the are either building peak of each block of cell membrane, metabolite such as choline, or in MRS direct/indirect products of cell metabolism, such as lactate and creatine, or neurotransmitters like NAA.

1. Choline: increased Cho with most tumor types, mainly owing to increased phosphocholine level. 2. Creatine: a marker for energy metabolism. relatively constant in vivo, serves as internal standard. 3. Lactate: only detectable within tumor cells, also increased in ischemia and hypoxic condition. 4. NAA: biomarker for brain tumors. Decrease in tumor.

1. No radiation. 1. Relative low signal to 2. Good spatial resolution noise ratio. (w2 mm). 2. Geometric distortion 3. Noninvasive technique to owing to eddy current reconstruct neural fiber and susceptibility bundles in vivo. artifacts. 4. Image biomarker for 3. Less specific compared white matter integrity. with PET tracer because microhemorrhage and scar tissues can also reduce diffusion. 1. Directly involve cell 1. Sensitive to system permetabolism. formance such as shim2. No radiation. ming, susceptibility, field homogeneity. 2. Low spatial resolution. 3. Trade-off between SNR, scan time and spatial resolution.

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DWI/DTI

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Table 1 (continued )

Imaging Techniques

Biological Processes

Quantitative Measures

13

Hyperpolarized Naturally occurring C Area under the 13 C-pyruvate in vivo is extremely low, peak of it makes 13C MRS a metabolite in unique technique to MRS quantify the rate of glycolysis by following the injection of 13Clabeled pyruvate.

CEST MR imaging

Area under the 1. There are continuous peak of CEST exchanges between spectrum water protons and compound protons, such as metabolites with -NH, -NH2, and -OH. 2. With special continuous RF saturation pulses to compound protons and exchanges with water protons, the final water saturation depends on the concentration of compound protons.

Pathophysiologic Correlations Pyruvate is associated with aerobic glycolysis, which is the main metabolic mechanism by tumor cells.

Advantages

1. Directly involves cell metabolism. 2. High signal intensity owing to hyperpolarization. 3. Ultrafast imaging owing to hyperpolarization, can be used for dynamic studies of the kinetic of metabolism between tumor and normal cells. 1. Any metabolites that 1. Directly relates to cell have group -NH, -NH2, metabolism. and –OH can be used for 2. In principle, any metabosource of CEST MR lite that has group -NH, imaging. -NH2, and –OH can be used for source of CEST 2. Most studied metabolite MR imaging. for CEST: amide protons (amide proton transfer) 3. Increase signal intensity by at least 2- fold for brain, glucose compared with conven(including –OH group) tional MRS. for metabolism.

Disadvantages 1. Complicated preparation procedure with a very short time window (w1 min) from preparation to injection. Need specially trained team. 2. Currently only available in a few academic centers equipped with dedicated equipment. 1. Long scan time and vulnerable to motion artifacts. 2. Variations in RF performance and field inhomogeneity can affect saturation transfer.

Abbreviations: CEST, chemical exchange saturation transfer; DTI, diffusion tensor MR imaging; DWI, diffusion-weighted imaging; 18F-FDG, 18F-2-deoxy-D-glucose; FET, 18F-methionine; 18F-FLT, 18F-fluoro-3’-deoxythymidine; MET, 11C-methionine; MRS, MR spectroscopy; NAA, N-acetyl aspartate; PSA, prostate-specific antigen; PSMA, prostate-specific membrane antigen; RF, radiofrequency; RT, radiation therapy; SNR, signal-to-noise ratio; SUVmax, maximum standardized uptake volume; TBR, tissue-to-blood ratio or tissue-to-background ratio; TK1, thymidine kinase 1; TMR, tissue-to-muscle ratio.

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 6. Diffusion MR imaging for RT. (A) Normal tissue. (B) Tumor detection. (C) Evaluation of tumor response to RT. (D) Potentially increasing sensitivity and specificity to normal appearing, non–contrast-enhanced parts of solid tumor using high b-value diffusion-weighted imaging. RT, radiation therapy. (Courtesy of Dr Yaniv Assaf, Tel Aviv University.)

heterogeneity, many active glioblastomas have non–contrast-enhanced subregions. Edema and postoperative inflammation often lead to overestimation in fluid-attenuated inverse recovery images and inconsistent ADC changes owing to a mixture of high cellular tumor cells, edema, and normal and scar tissues. This study applied DWIs of high b values of up to 3000 s/mm2 to 21 patients with glioblastoma underwent postresection chemoradiation. PTV definition was based on contrastenhanced T1-weighted images. Among 15 patients with posttreatment progression, 14 patients had incomplete dose coverage for the pretreatment hypercellularity volumes, which was defined based on DWIs with a b value of 3000 s/mm2 (Fig. 7). The nonenhanced hypercellularity volumes and the subvolume of hypercellularity volume that was not coverage by the prescription dose were significant negative prognostic indicators for the progression free survival. High b value DWI increases the sensitivity and specificity for detection of hypercellular components in high-grade glioblastoma and will improve accuracy of target delineation. High b value DWI is quick, typically 2 to 3 minutes, and can be readily implemented in most clinical MR imaging scanners.

For prostate cancers, multiparametric MR imaging has shown promising results for prostate cancer detection.25 Meanwhile, clinical applications of 68 Ga-PSMA PET showed improved detection of recurrent prostate cancer and better image contrast between tumor and normal regions with comparison to commonly used PET with 18Fcholine.41 A recent comparative study42 that acquired simultaneous 68Ga-PSMA PET and multiparametric MR imaging on a PET/MR imaging for a group of 53 patients with prostate cancer have shown statistically significant improvements in detecting prostate cancer. Compared with biopsy results (Fig. 8), simultaneous PET/MR imaging outperformed multiparametric MR imaging alone (area under curve, 0.88 vs 0.73; P<.001) and PET imaging alone (area under curve, 0.88 vs 0.83; P 5 .002). This study is an excellent example of synergies of PET/MR imaging. Meanwhile, recent developments in 13C-pyruvate MR imaging show promising results that increased 13C-pyruvate and 13C-lactate concentrations can be image biomarkers for an increased level of aerobic glycolysis in tumor cells with good sensitivity and specificity. Such regions could be escalated to higher RT doses. A recent clinical

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Fig. 7. High b value diffusion-weighted image for detection of hypercellularity in patients with a non–contrastenhanced subvolume of high-grade glioblastoma. For these patients, contrast T1 underestimated target volume owing to frequent nonenhanced tumor subvolume. In all 3 patients, there was no difference in depicting tumor volume between post-Gd T1 (red contour, also used as the target volume for radiation therapy) and conventional apparent diffusion coefficient (ADC) maps acquired with a b value of 1000 s/mm2. Fluid-attenuated inverse recovery images overestimated the tumor volume (green contour) owing to edema. When increasing b value to 3000 s/ mm2, it attenuates contributions from fast water diffusion, such as those from edema and normal tissues, and contributions from slow water diffusion from high-density tumor cells are enhanced (yellow contours; ie, pretreatment hypercellularity volume). Post-Gd T1 of the recurrent tumors (cyan and red arrows) clearly showed large overlapping between recurrence and the subvolumes of pretreatment hypercellularity volumes that were not covered by the radiation. T1W, T1-weighted image; T2W, T2-weighted image. (From Pramanik PP, Parmar HA, Mammoser AG, et al. Hypercellularity components of glioblastoma identified by high b-value diffusionweighted imaging. Int J Radiat Oncol Biol Phys 2015;92:816; with permission.)

study using hyperpolarized 13C-pyruvate MR imaging43 has proven its feasibility for clinical imaging of patients with prostate cancer. The feasibility of simultaneous 13C-pyruvate MR imaging/ PET on a clinical PET/MR imaging was also demonstrated by a recent canine study of liposarcoma.44 In tumors, both 18F-FDG-PET and 13Cpyruvate MR imaging showed increased uptake and increased lactate concentration, indicating increased aerobic glycolysis (Fig. 9A–C). More interestingly, increased uptake of 18F-FDG-PET was observed in muscles owing to dog exercise before imaging and, meanwhile, nonenhancement from 13C-pyruvate MR imaging clearly demonstrate the high specificity of 13C-pyruvate MR imaging in detection of aerobic glycolysis (see Fig. 9B vs Fig. 9D, E). Recently, studies of glucose chemical exchange saturation transfer (CEST) MR imaging, which uses

injection of a few grams of dextrose solution as the exogenous contrast, both in animal studies45,46 and in human patients with head and neck cancers47 have shown significantly increased glucose CEST signal in tumor regions with comparison to normal tissues. These interesting results raise questions about whether CEST MR imaging using endogenous metabolites or exogenous contrast agents can be a viable alternative to PET imaging with specific tracers that target similar metabolites or functional processes, for example, glucose CEST MR imaging versus 18FDG-PET (Table 2). Several preliminary studies have shown that CEST and PET information are complementary. Although current CEST MR imaging is impractical for initial whole body screening purposes, it is not unrealistic that the glucose CEST MR imaging can be used instead of 18FDG-PET for the early evaluation of treatment response during RT for tumors of

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 8. A 67-year-old patient with a biopsy-proven prostate cancer (red arrows), a Gleason score of 7 and a prostate-specific antigen of 5.4 ng/mL. (A, B) Axial T2-weighted MR image of apical sextants show slightly hypointense signal with restricted diffusion in the apparent diffusion coefficient map, but isointense impression in bvalue 800 s/mm2 in the transitional zone. (C) dynamic contrast-enhanced-MR imaging curve exhibits pronounced enhancement with a type 2 curve pattern resulting in a Prostate Imaging Reporting and Data System score of 3. (D) PET and (E) fused T2-MR imaging/PET images show intense focal uptake projecting ventrally to the urethra. Owing to the high intensity of 68Ga-PSMA-HBED-CC uptake both in PET and 68Ga-PSMA HBED-CC PET/MR imaging, a score of 5 was applied. (F) Hematoxylin and eosin gross section histopathology shows a corresponding tumor focus. (From Eiber M, Weirich G, Holzapfel K, et al. Simultaneous 68Ga-PSMA HBED-CC PET/MRI improves the localization of primary prostate cancer. Eur Urol 2016;70:832; with permission.)

known locations. If using CEST MR imaging for quantification of tumor metabolism, simultaneous PET acquisition of other tracers, such as 18FMISO or 18F-HX4 for hypoxia, can be performed.

Metabolic information from CEST MR imaging and hypoxic information from PET can be combined for better characterization of tumor heterogeneity and, ultimately, better treatment adaptation.

Fig. 9. Transaxial images of right front leg showing the liposarcoma. Note the high concentration of 18F-FDG in muscle (B, arrow; 18F-FDG-PET 1 1H MR imaging) and of 13C-pyruvate in the large vessels (D, arrow; 13C-Pyruvate CSI 1 1H MR imaging). (A) 18F-FDG PET. (B) 18F-FDG-PET 1 1H-MR imaging. (C) 1H-MR imaging. (D) 13C-pyruvate CSI 1 1H-MR imaging. (E) 13C-lactate CSI 1 1H-MR imaging. (From Gutte H, Hansen AE, Henriksen ST, et al. Simultaneous hyperpolarized (13)C-pyruvate MRI and (18)F-FDG-PET in cancer (hyperPET): feasibility of a new imaging concept using a clinical PET/MRI scanner. Am J Nucl Med Mol Imaging 2014;5:42; with permission.)

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Table 2 Comparisons between CEST and PET Parameters

CEST-MR Imaging

PET

Spatial resolution

In a scale of 1–2 mm, no intrinsic limitation but trade-off with Z-spectrum resolution and scanning time. 1. Typically, 1–2 min per coverage of the Z-spectrum for a slice location. 2. Long acquisition is main challenge for CEST. Intermediate sensitivity and typically micromolar to millimolar concentration. 1. Selective for exogenous CEST agents. 2. Less selective for endogenous agents owing to multiple factors such as temperature, pH values. 1. SAR limits owing to long duration of RF pulses if continuous RF technique is used. 2. Toxicity of some exogenous paramagnetic CEST contrast agents is current under investigation. 1. Long acquisition with motion artifacts. 2. A limited number of endogenous metabolites can be detected in 3T owing to small differences in chemical shift compared with that of water, such as metabolites with amine and hydroxyl groups. 3. Detection of more metabolites would be expected with increase of field strength but increased susceptibility could affect image quality and SAR in ultrahigh field can be hazardous.

In a scale of 4-5 mm and intrinsic limitations from scatter and detector size. Typically 2–4 min per bed station.

Temporal resolution Sensitivity Selectivity

Risks

Pitfalls

High sensitivity and picomolar tracer concentration. Selectivity defined by PET tracers.

Radiation dose from tracer is still a risk factor for pediatric and female patients at child-bearing age.

1. Low resolution with partial volume effect. 2. Significant delay, in scale of 2–6 h, between tracer injection and imaging for tracer uptake, redistribution, and clearance.

Abbreviations: CEST, chemical exchange saturation transfer; RF, radiofrequency; SAR, specific absorption rate. Adapted from Wu B, Warnock G, Zaiss M, et al. An overview of CESTMRI for non-MR physicists. EJNMMI Phys 2016;3:19.

PET/MR Imaging for Angiogenesis and Hypoxia Tumor angiogenesis is another hallmark biological process of cancer cells.15 Hypoxia is one of most important biological processes that modulates the outcome of RT and it originates from the tumor microenvironment, where the demand of the oxygen exceeds the supply of the oxygen. The level of tumor hypoxia is a prognostic indicator for the overall treatment of many types of solid tumors.48 Angiogenesis49,50 and hypoxia51–53 are highly interplayed biological processes in tumor. Tumor blood vessels are leaky, highly heterogeneous, and tortuous with complicated branching patterns. All these structural abnormalities lead to impairment of oxygen supply/exchange and consequently hypoxic condition. There are 2 basic types of hypoxia in vivo: chronic and acute hypoxia. Chronic hypoxia usually happens within the distance of 100 to 180 microns around blood

vessels and is caused by limited diffusion range of oxygen from tumor vessels to surrounding tissues. Chronic hypoxia lasts from hours to days and regions with chronic hypoxia are highly heterogeneous within the tumor. Acute hypoxia, in contrast, is mainly related to instability of blood flow owing to structural abnormalities in tumor vessels and consequent transient changes in perfusion. The oxygen pressure in acute hypoxic regions fluctuates over time and acute hypoxic regions are highly variant spatiotemporally. Oxygen is an excellent radiosensitizer that makes DNA damage from radiation permanent. Whether chronic or acute, reduction or absence of oxygen significantly increases radioresistance of tumor in hypoxic regions, by up to 3 times. Both hypoxia types contribute to development of more aggressive tumor phenotypes. Imaging of hypoxia can be important for RT for 2 main reasons. First, it can determine hypoxic status based on functional imaging information to

PET/MR Hybrid Imaging into Radiation Therapy enable stratification of patients for different treatment regimes, for example, targeted therapy or hypoxic radiosensitizers along with RT versus RT alone. Second, imaging can provide spatial distribution of hypoxic subregions within the tumor and can potentially quantify severity as well as monitor dynamic changes of hypoxia. With recent advances in high-precision delivery techniques, such as IMRT and VMAT, and high precision imaging guidance during treatment, including on-board daily cone beam CT, there is a growing interest in hypoxic image-guided radiation dose painting to provide selective dose escalation to radioresistant hypoxic subregions, without overdose surround normal tissues. PET imaging, using the hypoxia-specific nitroimidazoles derivatives 18FMISO, 18F-fluoroazomycin arabinoside (18F-FAZA), and HX4, can play an important role in hypoxia imaging-guided RT. So far, the most studied cancer type with PET hypoxia image–guided dose painting is head and neck cancers. A brief summary of published studies including main results is provided in Table 3. PET hypoxia image–guided dose painting was recently extended to other cancer types, such as HX4 PET for non–small cell lung cancer.66 All these studies have shown that hypoxia image–guided radiotherapy is technically feasible and can improve therapeutic ratio. There are 2 common approaches to achieve dose escalation to radioresistant tumor subvolumes using dose painting. The first is a uniform dose painting (Fig. 10B), where a uniform dose is delivered using IMRT/VMAT techniques to PET avid regions. Usually, a threshold value based on tumor-to-blood or tumor-to-muscle ratio is used to segment out PET avid hypoxic subregions. Instead of delivering uniform escalating dose to all PET avid regions, dose painting by number creates a map of locally varying dose escalation factors that are determined by a biological model based on dynamic 18FMISO PET images (see Fig. 10C). Using a dose painting by number strategy created by Thorwarth and colleagues55 as an example, a TCP model was developed. The model assumed the local radioresistance of tumor is proportional to the grade of tracer retention and also assumed the time for reoxygenation in hypoxic regions is inversely proportional to the vascularization–perfusion parameters of the hypoxic microenvironment. Therefore, at each voxel location, this model linked the expected number of survival cells at the end of RT, that is, tumor control, to the voxelwise radioresistance quantified by 18 FMISO PET, through a derived dose escalation factor. The final voxelwise dose prescription was then generated. Both dose painting approaches

have their advantages and drawbacks. A quick summary is provided in Table 4. Although promising, there are several technical challenges for PET hypoxia image–guided RT. Developing a robust quantitative model that associates image intensity in hypoxia images with the tissue oxygenation level in vivo remains an active research area. By observing the hypoxia criteria using by most studies so far (see Table 3), a rather empirical threshold value of the tissue-to-blood ratio and tissue-to-muscle ratio was often adapted to segment the hypoxic region in images. So far, image-derived threshold values have not been validated against histologic or pathologic results. The TCP model-based approach for dose painting by number has big advantages of biological relevance. However, validation studies are needed to validate model assumptions about tumor uptake level of PET image and the true radioresistance of tumor cells. If model assumptions are proven to be accurate, dose painting by number, compared with uniform dose painting, will have more strength in terms of achieving tumor control while maintaining the same OAR dose constraints. Another major challenge for PET hypoxia image– guided RT comes from limitations of PET tracers themselves. In general, it takes on average 2 to 4 hours for a nitroimidazole-based tracer to clear out normal tissues and achieve optimal tumor-toreference tissue contrast. 18FMISO will take a longer clearance period owing to its lipophilicity. Therefore, it is commonly recognized that PET images characterize mostly chronic hypoxia, instead of acute hypoxia, where the hypoxic situation can vary on a scale of less than a few hours. Inconsistent data about the temporal variation and repeatability of different PET hypoxia imaging modalities were published. Although significant changes in hypoxia distributions were observed in one-half of enrolled patients in a study by Lin and colleagues57 with 2 18FMISO scans acquired 3 days apart, no changes in the maximum standardized uptake value, tissue-to-blood ratio, tissue-tomuscle ratio or segmented hypoxia volumes between longitudinal 18FMISO images acquired in a 2-day interval by another study.67 Temporal variation of tumor hypoxia during fractionated RT can be complicated owing to tissue reoxygenation under treatment.68,69 Longitudinal PET hypoxia images before and during radiation treatment can potentially characterize the dynamic of tumor hypoxia more accurately and then, adaptive RT, which represent adjustments of radiation treatment plan based on updated evidence of tumor response from imaging evidence, can be made during treatment to ensure the best treatment outcome. Radiobiology models for adaptive

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Table 3 Hypoxia image–guided dose painting studies for head and neck small cell carcinoma Patient Number

PET Tracer

Hypoxia Criteria

Dose Escalation

Evaluation

Note

NA

Cu-ATSM

TMR >2

80 Gy

Planning study, no TCP modeling.

Thorwarth et al,55 2007

12

18

DPBN

Up to 20% boost

Planning study. Mean 14.3% increase in TCP.

Grosu et al,56 2007

18

FAZA

TMR >1.5

80 Gy

Lin et al,57 2008

7

18

FMISO

TBR >1.3

84 Gy

Planning study, no TCP modeling. Planning study, no TCP modeling.

Lee et al,58 2008

10

18

FMISO

TBR >1.3

84 Gy

Planning study, no TCP modeling.

Bowen et al,59 2009

3

Cu-ATSM

DPBN

Up to 90 Gy

Planning study, no TCP modeling.

Choi et al,60 2010

8

18

Tumor/cerebellum Ratio >1.3

78 Gy

Planning study, no TCP modeling.

First hypoxia image–guided dose painting, showing the feasibility of dose painting. Quantitative comparison of 2 dose painting approaches: uniform dose painting to subvolume and DPBN. First feasibility study with FAZA imaging of hypoxia in HNSCC. Longitudinal 18FMISO scans with 3 d apart. Change of tumor hypoxia pattern compromised the coverage of hypoxic tumor volumes by dose painting. Feasibility study. Dose escalation to 84 Gy was successful for all patients without violate normal tissue tolerance. One patient has escalation up to 105 Gy. Methodology paper about improving accuracy for dose painting. For 6 of 8 patients, dose painting was feasible without violate normal tissue tolerance. Dose painting was not feasible in 2 patients owing to close vicinity of critical structures.

Authors 54

Chao et al,

2001

FMISO

FMISO

10

18

FMISO

NA

80–90 Gy

Planning study, mean 17% increase on TCP.

Toma-Dasu et al,62 2012

7

18

FMISO

4 regions based on PO2 map.

Up to 121 Gy

Planning study, no TCP modeling.

Chang et al,63 2013

8

18

FMISO

TMR >1.5

84 Gy

Planning study, mean 20% increase in TCP without changes in NTCP.

Henriques de Figueiredo et al,64 2015

20

18

FMISO

Adaptive Bayesian segmentation

79.8 Gy

Servagi-Vernat et al,65 2015

12

FAZA

>Background mean 1 3 SD

86

Planning study, 18.1% increase in TCP, 4.6% increase in parotid NTCP. Planning study, no TCP modeling.

Biological model-based evaluation of simultaneous integrated boost with dose painting. An algorithm was developed to quantify radiosensitivity level of tumor based on the 18FMISO map. Prescribed dose for dose painting was decided based on imagederived radiosensitivity and a predefined tumor control level. Plan evaluation through biological modeling. Dose painting based on 18 FMISO imaging is superior to uniform dose escalation in terms of both TCP and NTCP. Automatic local fuzzy Bayesian method to extract hypoxic volume from 18FMISO map. Dose escalation up to 86 Gy to hypoxic volumes did not modify the dose metrics on the surrounding normal tissues.

Abbreviations: DPBN, dose painting by number; FAZA, 18F-fluoroazomycin arabinoside; 18FMISO, 18F-fluroromisonidazole; HNSCC, head and neck squamous cell carcinoma; NA, not applicable; NTCP: normal tissue complication probability; TBR: tissue-to-blood ratio; TCP: tumor control probability; TMR: tissue-to-muscle-ratio. Adapted from Yuan H, Li Z, Chang S. Hypoxia image guided radiation therapy: current status and major challenges. Austin J Nucl Med Radiother 2016;3:1014.

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Hendrickson et al,61 2011

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Fig. 10. Hypoxia image–guided radiotherapy with dos painting. (A) A typical target volume for head and neck small cell carcinoma includes 3 dose levels, covering standard risk planning target volume (PTV; blue contour in 54 Gy in 30 fractions), intermediate risk PTV (yellow contour receiving 60 Gy), high-risk PTV (red contour receiving additional 10 Gy boost). (B) Uniform dose escalation of 10% (ie, 77 Gy) to 18F-FDG PET positive area (in pink). (C) Dose painting by numbers according to the superimposed dose-escalation map determined from dynamic 18FMISO scans. The dose level at each voxel within the 18FMISO avid region depends on the radioresistance level quantified by a biological model using hypoxic level from the 18F-fluroromisonidazole map. (From Thorwarth D, Eschmann SM, Paulsen F, et al. Hypoxia dose painting by numbers: a planning study. Int J Radiat Oncol Biol Phys 2007;68:296; with permission.)

therapy as well as several practical factors, such as optimal time interval for imaging, remain active research topics. Aside from PET, functional information from several MR imaging modalities directly or indirectly quantify tumor angiogenesis and hypoxia. In contrast with direct correlation between PET tracer concentration and hypoxia-related events at the molecular level, MR hypoxia imaging reflects changes in the macroenvironment and the

microenvironment that lead to or are caused by tumor hypoxia. For example, DCE-MR imaging quantifies permeability of tumor blood vessels as well as blood perfusion, whereas BOLD and TOLD MR imaging reflect blood oxygenation level in tumor venous vessels, and in the plasm and interstitial space respectively (Fig. 11). The oxygen and deoxyhemoglobin (dHbO2) are paramagnetic, whereas oxyhemoglobin (HbO2) is diamagnetic. Both TOLD and BOLD can be used for imaging

Table 4 Comparison of uniform dose painting and dose painting by number Parameters

Uniform Dose Painting

Dose Painting by Number

Plan complexity

1. Easy implementation 2. Plan optimization is relatively easy 1. Need segmentation for defining hypoxic regions. 2. Segmentation usually arbitrary, more empirical and less robust.

Plan complexity owing to voxelwise varying dose prescription. 1. No need for segmentation. 2. Dose escalation based on voxelwise hypoxic level through biological modeling, more robust and more biological relevant. Voxelwise dose prescription, sensitive to misregistration

Hypoxic region segmentation

Sensitivity to misregistration Target coverage

1. Uniform dose to a hypoxic volume. 2. More tolerable to misregistration between PET and planning CT. Keeping same OAR dose limits, target coverage is less optimal owing to uniform high dose to the whole volume of hypoxic regions.

Abbreviations: CT, computed tomography; OAR, organ at risk.

Keeping same OAR dose limits, target coverage is better owing to spatial varying dose level according to hypoxic levels.

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 11. Imaging principles of MR imaging modalities for tumor angiogenesis and hypoxia. (A) A typical time course of MR imaging signal within a voxel following bolus injection is displayed, which typically including the contrast baseline (from time 1–2), contrast accumulation phase (time 2–3) and contrast washout phase (after time 3). When bolus-carrying blood flow arrives at time point 2, the MR imaging signal increases gradually mainly contributed by Gd molecules (the gray dots in Fig. 11A) in blood and the gradually accumulated Gd in the extracellular and extravascular space (EES) if vessels are leaky. Because bolus-carrying blood is constantly moving so do the Gd molecules in blood, the signal during the majority of the accumulation phase of the MR imaging signal is mainly contributed by contrast agents in EES. Therefore, the peak intensity of MR imaging and how fast the signal increases depends on the blood flow and the permeability of the vessels. During the washout phase, the Gd molecules in EES will come back into vessels for excretion through kidney or liver and, consequently, the MR imaging signal begins to decrease gradually after time 3. Dynamic contrast-enhanced-MR imaging quantifies vessel permeability and fractional tumor volume through a 2compartment pharmacokinetic model that monitors dynamic intensity changes of T1-weighted images after injection of an exogenous contrast agent. (Adapted from Jackson A, Buckley D, Parker GJM, editors. Dynamic contrast-enhanced magnetic resonance imaging in oncology. Berlin: Springer-Verlag; 2005.) (B). MR imaging modalities, such as blood oxygenation level dependent (BOLD) and tissue oxygenation level dependent (TOLD) modalities, are sensitive to endogenous contrast agents, such as oxygen and deoxyhemoglobin, owing to their paramagnetic properties. TOLD relies on oxygen effect on T1 relaxation. Increased oxygen concentration in plasm and the interstitial space will reduce T1 relaxation time of tissue and, therefore, hyperintensity in T1-weighted images. With inhalation of hyperoxic gas, normoxic tissue will have increased T1 signal owing to excess unbound free oxygen in plasm and the interstitial space (B.3), whereas no enhancement will be found in hypoxic tissue owing to lack of unbound free oxygen (B.5). BOLD relies on susceptibility effect from paramagnetic deoxyhemoglobin on T2 and T2* of tissues near venus vessels. With hyperoxic gas, an increased ratio between oxyhemoglobin and deoxyhemoglobin in the venous vessels of normal tissue will increase T2 and T2* of surround tissues and therefore increase signal intensity (B.4). Much less enhancement will be found with hypoxic tissue owing to more deoxyhemoglobin in hypoxic blood (B.6). Hb, hemoglobin.

hypoxia, normally through breathing in hyperoxic gas to enhance contrast between normoxic and hypoxic tissues. In hypoxic tissues, a low concentration of blood oxygen will not fully oxygenate

hemoglobins and, therefore, no T1 enhancement to hypoxic tissue owing to low oxygen concentration in either plasm or interstitial space. Because hemoglobin has a longer T1 than HbO2 and the ratio

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induced tumor growth delay. A recent study by O’Connor and colleagues82 further validates the feasibility of oxygen-enhanced TOLD MR imaging to quantify spatial variations of hypoxia in vivo through a preclinical colorectal xenograft model. They observed a significant correlation between oxygenation level of the air that mice breathed in and R1 values of tumor measured by TOLD (Fig. 12A), which also mirrors the oxygenation level detected by the Oxylite measurement (see Fig. 12B). Interestingly, when combining DCE-MR imaging and oxygen-enhanced TOLD MR imaging, MR imaging can distinguish 3 different subpopulations with different physiologic status of necrosis (nonperfused), normoxia, and hypoxia. MR imaging-based segmentation matched with histologic results with pimonidazole staining (see Fig. 12C) and the hypoxic fraction within the tumor identified by MR imaging significantly correlated with hypoxic fraction identified by pathology results (see Fig. 12D). In summary, both PET and MR imaging can provide important information on tumor angiogenesis and hypoxia (Table 5). Several studies83–85 show convincing evidence of complimentary information on hypoxia from PET and MR imaging modalities acquired separately, which may be leveraged by simultaneous acquisition with a hybrid PET/MR imaging system. With PET/MR imaging, it is reasonable to expect several breakthroughs will happen, especially toward (a) using a multiparametric PET/MR imaging protocol with improved spatial and temporal solution for better characterization of complicated mechanism of hypoxia, and (b) longitudinal monitoring of hypoxia status during RT for hypoxia image–guided dose painting to achieve greater radiobiological effectiveness.

PET/MR Imaging for Better Characterization of Tumor Heterogeneity Tumor heterogeneity is a well-recognized characteristic of cancer biology.86 It is also one of most important biological factors that modulate outcomes of RT.87 Radiation response can vary substantially among patients of the same pathologic tumor subtype, often referred as intertumor heterogeneity, and is driven mainly by genetic variations in tumors. This leads to the clinical necessity of using functional and molecular information from MR imaging and PET to evaluate subject-specific tumor metabolism, proliferation, and hypoxia as well as treatment response to achieve the best treatment strategy for patients. More interestingly, even within tumors of the same patient, tumor biology studies86,88 have revealed that there are substantial spatial variations in gene expression,

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 12. Imaging hypoxia with oxygen-enhanced tissue oxygenation level dependent (TOLD) MR imaging (OE-MR imaging). (A) Longitudinal relaxation rate (R1) measured in preclinical xenografts models is stable when breathing air, but shows significant increase during oxygen inhalation. (B) OE-MR imaging signal changes mirror the time course of change in tumor PO2 detected by Oxylite measurement in the same xenografts (maximum to the detection range was 100 mm Hg). (C) Dynamic contrast-enhanced-MR imaging is used to identify perfused tumor and then OE-MR imaging maps are analyzed for hypoxic tumor, defined by lack of positive change with oxygen challenge. Representative MR imaging maps are shown in an SW620 colorectal cancer xenograft, along with a segmentation that defines nonperfused, normoxic, and hypoxic tumor subregions. Companion data are from an immunofluorescence-based assay of pimonidazole adduct formation. (D) The hypoxic fraction defined by MR imaging closely correlates with the equivalent measurement defined by pathology, providing initial biological validation of the technique. (From O’Connor JPB. Cancer heterogeneity and imaging. Semin Cell Dev Biol 2016. http://dx.doi.org/10.1016/j.semcdb.2016.10.001 (in press, corrected-proof); with permission.)

biochemistry and microenvironment, angiogenesis, and hypoxia. This underlying tumor biology leads to spatially and temporally different radiosensitivity and radioresistance, often referred as intratumor heterogeneity (Fig. 13). Increased understanding of tumor heterogeneity motivates a paradigm shift within the medical oncology field toward precision medicine that customizes treatment for each individual based on his or her genetic, proteomics, metabolomics, and medical imaging information to address tumor heterogeneity and maximize overall treatment outcomes. Compared with to genomic and proteomic information, phenotypic information from medical imaging has unique and complementary advantages. Most imaging techniques are noninvasive or at most minimally invasive. Phenotypic information from imaging covers the whole

tumor volume, whereas techniques to obtain genomic and proteomic information are usually invasive and are subject to sampling or biopsy bias, especially with the existence of intratumor heterogeneity. Multimodality functional imaging, such as MR imaging and PET, provides distinct but complementary quantifications of tissue characteristics within the whole tumor, which can be further associated with underlying gene expression patterns. To address tumor heterogeneity in RT, research and clinical applications so far mainly focused on imaging for intertumor heterogeneity owing to relatively straightforward study design, including PET/ MR imaging for tumor hypoxia and metabolism, as discussed in previous sections. Meanwhile, there is increasing interest for imaging intratumor heterogeneity, especially with recent advances in

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Table 5 PET/MR imaging techniques for hypoxia in radiation therapy Imaging Techniques Biological Processes

Quantitative Measures

Pathophysiologic Correlations

Advantages

Disadvantages

FMISO/ Under hypoxic condition, SUVmax TBR FAZA/HX4 tracers will bind with TMR proteins and RNAs, which lead to intracellular accumulation of tracers.

Concentration of tracers proportional to severity of hypoxia.

1. Directly involve hypoxia 1. Low spatial resolution induced molecular (4 mm and above). events. 2. Long postinjection in2. Reasonable TBR. terval (2–4 h) before 3. High sensitivity at the reaching reasonable picomolar level. TBR. 3. Mainly chronic hypoxia.

Cu-ATSM

SUVmax Under hypoxic condition TBR and depending on TMR cellular pH value, instable [64Cu1ATSMH] will react with intracellular proteins and be trapped inside cells.

Concentration of tracers proportional to severity of hypoxia.

1. Directly involve hypoxia- 1. Low spatial resolution induced molecular (4 mm and above). events. 2. Less specific and 2. Reasonable TBR, high affected by mechanisms membrane permeability other than hypoxia, such and fast tumor uptake, as local blood perfusion short imaging delay owing to its high postinjection. lipophilicity. 3. High sensitivity.

DCE-MR imaging

Linear relationship 1. ne/np, quantify voxelwise 1. ne increases in tumor. volume of tumor vol2. Ktrans depends on the between altered tissue complex interactions ume and tumor vascular T1 with existence of Gd, between blood perfunetwork. and the concentration of sion and vessel perme2. Ktrans/Kep, are the transGd molecules within fer rates of Gd molecules ability. Ktrans quantifies tissues. tumor vascularity and from plasm space to potentially hypoxia extracellular extravasowing to abnormal tucular space, and the remor vessel network. turn rate of the reverse direction.

1. Low toxicity of contrast 1. Indirect quantification agents and suits for lonof hypoxia. gitudinal studies. 2. Complexity in modeling, 2. No radiation. such as variation in 3. Good spatial resolution arterial input function (w2 mm). and T1 measurement. 4. Good temporal 3. Trade-off between resolution. spatial and temporal resolution.

Paramagnetic Gd contrast reduces T2* of surrounding tissues, agent inside vessels can affect tissues within a considerable distance outside of vessels.

BOLD

Deoxyhemoglobin has Change in R2* significant effects on T2 and T2* of surrounding tissues owing to susceptibility differences.

TOLD

Increased oxygen Change in R1 concentration in plasm and interstitial space reduces tissue T1 and leads to hyperintensity on T1-weighted images.

Blood volume and blood flow.

DSC-MR imaging is an excellent modality to quantify hemodynamics of circulation, such as blood flow and blood volume.

1. Low toxicity and suited for longitudinal studies. 2. No radiation. 3. Good spatial resolution (w2 mm). 4. Good temporal resolution. 5. Excellent for brain tumors with diffused capillary bed but without significant breakdown of blood– brain barrier. With hyperoxic gas, 1. Endogenous contrast increased ratio between agent and suits for lonoxyhemoglobin and gitudinal studies. deoxyhemoglobin in 2. No radiation. venous vessels of normal 3. Good spatial resolution tissues increase T2* of (w2 mm). surround tissues. Much 4. Good temporal less enhancement with resolution. hypoxic tissue owing to more deoxyhemoglobin. With hyperoxic gas, 1. Endogenous contrast normoxic tissue will have agent and suits for lonincreased T1 signal gitudinal studies. owing to excess unbound 2. No radiation. free oxygen in plasm and 3. Good spatial resolution interstitial space and no (w2 mm). enhancement will be 4. Good temporal found in hypoxic tissue resolution. owing to lack of unbound free oxygen.

1. Indirect quantification of hypoxia. 2. When vessels are leaky, T1 effects cause uncertainty in modeling. 3. Susceptibility and eddy current artifacts.

1. Indirect quantification of hypoxia. 2. Susceptibility to eddy current artifacts. 3. Relative low signal intensity and low sensitivity.

1. Indirect quantification of hypoxia. 2. Relative low signal intensity and low sensitivity.

Abbreviations: BOLD, blood oxygenation level–dependent; DCE-MR imaging, dynamic contrast-enhanced MR imaging; DSC-MR imaging, dynamic susceptibility contrast MR imaging; FAZA, 18F-fluoroazomycin arabinoside; 18FMISO, 18F-fluroromisonidazole; TBR, tissue-to-blood ratio; TOLD, tissue oxygenation level–dependent; TMR, tissue to muscle ratio.

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Fig. 13. Intratumor heterogeneity is illustrated by histologic images of a non–small cell lung cancer showing spatial variation patterns in staining for angiogenesis (CD34), hypoxia (pimonidazole [PIMO]) and glucose metabolism (glucose transporter protein 1 expression [GLUT-1]). More interestingly, for the same voxel locations, cells have different combinations of metabolic, hypoxic, and angiogenesis status, indicating a different subpopulation of necrotic, hypoxic, and proliferative status. Multimodality imaging can potentially provide image evidence to segment a different subpopulation. (From Davnall F, Yip CS, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2012;3:574; with permission.)

both multimodality imaging techniques and functional image-guided RT. In this section, we discuss how image information is used for quantifying intratumor heterogeneity with a focus on synergies of hybrid PET/CT toward improving studies of tumor heterogeneity. The initial applications of imaging for RT mainly focused on tumor volume delineation from anatomic imaging modalities and treatment response evaluation based on size changes as recommended by RECIST and the World Health Organization (Fig. 14A). In those applications, information about intratumor heterogeneity is either unavailable from anatomic imaging modalities or largely ignored. With increasing applications of functional modalities, pathophysiologic information of tumors as well as their spatial and temporal variations within tumors becomes available and opens a new research area of image-based quantification of tumor heterogeneity. Although heterogeneity was not included as part of the criteria from RECIST, a few quantitative measures for tumor texture have been incorporated into several cancer-specific diagnostic reporting systems, such as the Breast Imaging Reporting and Data System (BI-RADS), the Prostate Imaging Reporting and Data System (PI-RADS), and the Lung Imaging Reporting and

Data System (Lung-RADS). Although most current functional imaging studies for RT still use summary statistics of functional parameters over the whole tumor for response evaluation and outcome prediction (see Fig. 14B), such as mean, median, max of Ktrans from DCE-MR imaging, or standardized uptake values from 18F-FDG-PET, there are increasingly active research projects on exploring more mathematical quantification of tumor heterogeneity, as indicated by the steadily increasing number of publications in recent years, that is, from 8 papers in 2006 and 2007 to 66 publications in 2012 and 2013.89 Detailed descriptions of mathematical principles of these different methods are beyond the scope of this article, and we encourage readers to read a few excellent review articles for details.89–91 A brief discussion of the major developments in image-based quantification of tumor heterogeneity is provided here, along with a summary of their advantages and drawbacks in Table 6. In general, tumor heterogeneity is quantified by 3 categories of image-based analysis (see Fig. 14C). The first category is histogram-based analysis. Quantitative measurements, from simple summary statistics of mean and maximum values to statistical quantities about the shape of the histogram, such as skewness and kurtosis, are

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 14. Quantifying intratumor heterogeneity. The example liver metastasis from a patient with a colonic primary tumor can be measured in several different ways. (A) Most clinical assessments of tumors are size based. (B) Functional imaging methods can measure tumor pathophysiology, but tend to derive average parameter values, such as median Ktrans. (C) Some intratumor heterogeneity methods quantify overall complexity of a distribution (histograms) or spatial arrangement of data (texture analysis). Other methods identify tumor subregions using a priori assumptions (partitioning) or data-driven approaches (multispectral analysis). 1D, 1-dimensional; 2D, 2-dimensional; DCE-MRI, dynamic contrast-enhanced MR imaging; RECIST, Response Evaluation Criteria in Solid Tumors; WHO, World Health Organization. (From O’Connor JPB, Rose CJ, Waterton JC, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 2015;21:250; with permission.)

derived. Although histogram analysis is easy to be implemented and can quantify the overall level of heterogeneity in tumor functions, for example, more heterogeneous tumor might have increased skewness or kurtosis, spatial information is totally discarded in histogram analysis. In contrast, methods in the second category estimate the so-called texture feature from images, which incorporate spatial locations of image voxels as well as their corresponding intensity values into unified mathematical quantification. These methods are often referred as texture analysis and they quantify spatial complexity of functional image. An image texture commonly describes (1) intensity difference among neighboring voxels,

that is, image contrast, (2) how big is the area with a selected contrast level, and (3) whether there is a directionality for contrast patterns. There are several different levels of texture analysis with increasing model complexity but also increasing abundancy of image features that potentially lead to better characterization of underlying tumor heterogeneity (see Table 6). Using the most commonly used grey-level cooccurrence matrix (GLCM),92 as an example, there is a 2-dimensional image with N unique grey-scale intensity values. The texture pattern along a selected direction, for example from left to right, of this image can be represented by an n  n matrix. The value of each matrix element, ni,j, represents how many times in the

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Table 6 Comparisons among image-based quantification methods for tumor heterogeneity Methods Histogram analysis

Quantitative Measures

1. Mean/median/maximum and standard deviation: overall descriptive statistics of histogram 2. Skewness/kurtosis: asymmetry/flatness of histogram 3. Uniformity/entropy: uniformity/irregularity of intensity Texture 1. Gray level co-occurrence Analysis matrix–based analysis Entropy/homogeneity (randomness/ uniformity of CM) 2. Run-length matrix based analysis Run length nonuniformity (small if spatial variation of intensity is small) 3. Neighborhood grey tone difference matrix Contrast (number of local variations in the image) Coarseness (measurement of edge density) 4. Fractal analysis Multispectral 1. Use quantitative meaanalysis sures from functional imaging modalities, such as TBR, SUV from PET, Ktrans from DCE-MR imaging, ADC from DWIMR imaging. 2. Threshold value for clustering or segmentation.

Advantages

Disadvantages

1. Easy implementation and abundant statistical tools available. 2. Relatively easy interpretations.

1. Disregard all spatial location of heterogeneity. 2. High-order parameters, such as nth percentile, has no direct biological interpretation.

1. Maximize information 1. Heavily depend on from single image motruthfulness and quality dality. Often no need of of imaging data. multiimage modalities. 2. Most quantitative mea2. High dimensional image sures lack of obvious characteristics that are biological meaning. not visible by naked eyes. 3. Potential model 3. Most methods are autooverfitting. matic without human interaction.

1. Take advantage of unique but complementary functional imaging information. 2. Highly biological relevant and translational if validated. 3. Easy implementation with multiple software tools available.

1. Require multiple imaging modalities and more expensive. 2. Potential redundancy in functional imaging info. Need more validation. 3. Uncertainty with determination of threshold values.

Abbreviations: ADC, apparent diffusion coefficient; CM, cooccurrence matrix; DCE-MR imaging, dynamic contrastenhanced MR imaging; DWI-MR imaging, diffusion-weighted MR imaging; SV, standardized uptake value; TBR, tissueto-blood ratio.

image, a voxel with a jth intensity value is the neighbor to the right of a voxel with an ith intensity value. This matrix is called GLCM. If generating GLCMs along multiple directions, such as left– right, up–down, and diagonal, the spatial pattern of intensity difference within the image can be preserved in these GLCMs. Several higher order statistics can be derived from these GLCMs to quantify spatial heterogeneity of image intensities (see Table 6). If considering a selected length of

consecutive neighboring voxels with the same intensity value, which is also referred as “a run” in the terminology of texture analysis, a run–length matrix instead of a GLCM can be generated for characterization of texture feature. For example, images with a fine feature or a fine resolution will have more short runs compared with more long runs from an image with a coarse feature. More sophisticated models are developed to describe the number of local variations within the image, such

PET/MR Hybrid Imaging into Radiation Therapy as the neighborhood grey-tone difference matrix, to quantify irregularity or roughness of a texture surface based on fractal analysis. The main advantage of texture analysis is its high efficiency to extract high dimensional data from images and enable more extensive and comprehensive characterization of image intensity pattern than methods of other categories. It can detect intensity change patterns that are not easily detectable by the naked eyes, even those trained eyes of radiologists. However, most quantitative measures of texture analysis do not have a clear biological or physiologic description, which sometimes make its translation to clinical applications difficult. In addition, texture analysis relies on voxelwise information for extract useful image feature and, therefore, it is less robust when the image quality is less than optimal. The last category is multispectral image analysis that segments the tumor into functionally unique subvolumes based on multiple imaging modalities. Owing to complicated tumor biology, different tumor subvolumes not only share some common pathologic mechanisms, but also have their unique signature biological processes determined by unique genetic mutations. For example, necrotic and hypoxic subregions both have low intensity on PET hypoxia imaging compared with nonhypoxic tumor regions. Meanwhile, a necrotic region will have a high diffusion signal in DWI MR imaging owing to death of tumor cells compared with a low diffusion signal for viable tumor cells no matter of their hypoxic status. Therefore, image analysis of single modality, as often used in texture analysis, will not accurately render tumor heterogeneity. Taking advantage of unique but complementary characterizations of tumor from multiple functional modalities, the tumor will be segmented into functionally coherent regions according to similar signal intensity (or spectral) of each modality. Then, cluster analysis can identify voxel clusters in a multidimensional spectral space by combining multiple imaging data. The main strength of this method comes from its high biological relevance and relatively easy translation to clinical application if validated, especially with synergies from multimodality data of a hybrid PET/MR imaging study. A recent study by Schmitz and colleagues93 demonstrates an excellent example of decoding intratumor heterogeneity of breast cancer with multispectral analysis of PET/MR imaging data. In this study, sequential 18 F-FDG-PET and DWI MR imaging data from 26 tumors were first acquired from mice models of polyoma middle-T transgenic breast cancer. For each image modality, a Gaussian mixture model with the Akaike or Bayesian information criteria for model selection was developed to automatically

categorize tumor voxels into multiple subgroups with unique tissue-to-muscle ratio values of PET or ADC values of DWI (Fig. 15). Based on combinations of these unique tissue-to-muscle ratio and ADC values, tumors were segmented into subvolume of cystic hyperplasia (ADC >1.0 mm2/ms), solid scinar (tissue-to-muscle ratio of 18F-FDGPET >9.6), and solid nodular tissue (ADC <0.6 mm2/ms and tissue-to-muscle ratio <5.1). Phenotypic maps from multispectral analysis were further validated with histologic results. More important, this study further translated this technique verified through the preclinical study to a human study, where simultaneous PET/MR imaging data were acquired from 5 patents with biopsy-proven breast cancer. Multispectral analysis accurately distinguished several subvolumes with heterogeneous diseases. Multispectral analysis with combined PET/MR imaging data was also successfully adapted in a recent preclinical study of a non–small-cell lung cancer xenograft model.94 Unique subvolumes of viable, necrotic, and hypoxic tumor populations were identified and further validated with histologic results.

PET/MR Imaging for Treatment Assessment and Adaptive Radiotherapy One of most important imaging applications in RT is for treatment assessment. Treatment assessment includes 2 equally important perspectives, treatment response of tumors and radiationinduced injury/toxicity in normal tissues. Although size-based assessment based on anatomic CT/MR imaging acquired several months after RT currently remains the main approach for treatment response of tumors, clinical values of early response evaluation during treatment based on metabolic information from 18 F-FDG-PET have been well-accepted in RT field. Although initial purpose of using longitudinal 18FFDG-PET for treatment response as recommended by the PET Response Criteria in Solid Tumors8 was mainly for chemotherapy, there are increasing numbers of preclinical and clinical studies of 18FFDG-PET for early treatment response of radiation treatment for brain, lung, head and neck, rectal, esophageal, and cervical cancers. However, for RT, radiation-induced inflammatory responses are often onset even in the early stage of treatment course. Increased uptake of 18F-FDG with inflammation makes 18F-FDG-PET less specific for early treatment response. Exploratory studies of MR imaging-based, PET proliferation, and hypoxia imaging for early evaluation have been carried out to overcome limitations of 18F-FDG-PET with promising results. Conclusions from some key studies

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Fig. 15. Multispectral analysis of combined PET/MR imaging for study of intratumor heterogeneity. (Top, preclinical mice model) 18F-2-deoxy-D-glucose (18F-FDG) and apparent diffusion coefficient (ADC) maps were categorized into clusters using a Gaussian mixture model. The combination of both criteria identified 3 distinct phenotypes: solid acinar tumor regions (18F-FDG tissue-to-muscle ratio [TMR] >9.6), cystic hyperplastic regions (blue; ADC >1.0 mm2/ms), and solid nodular regions with a double-negative pattern (green; ADC <0.6 mm2/s and 18 F-FDG TMR <5.1). (Bottom; human study) Combined 18F-FDG PET and diffusion-weighted MR imaging could distinguish several regions within heterogeneous disease: a representative tumor, composed of multiple foci of invasive carcinoma (IC) and a fibroadenoma component. In slice 1, the aggressive spots of the IC were identified by the high 18F-FDG population (red; standardized uptake value [SUV] >2.6). The fibroadenoma in slice 2 could be distinguished from the rest of the tumor by a low 18F-FDG accumulation (blue; SUV 0.9–1.6) combined with high ADC values (ADC >1.1 mm2/s). Histologic slices are shown in 100 magnification and the inset in 400 magnification. H&E, hematoxylin and eosin. (Adapted from Schmitz J, Schwab J, Schwenck J, et al. Decoding intratumoral heterogeneity of breast cancer by multiparametric in vivo imaging: a translational study. Cancer Res 2016;76:5512–22; with permission.)

of early treatment response using PET and MR imaging are highlighted in Table 7. Normal tissue toxicity is the main dose-limiting factor for tumor control with RT. It is also of special importance to minimize normal tissue dose for tumor types with long-term tumor control after RT, such as prostate, breast, and low-grade glioma. For imaging assessment of normal tissue toxicity, although 18F-FDG-PET studies have demonstrated its values on study radiation induced pneumonitis, damage to the myocardium and pelvic bone marrow and, in general, mechanisms of currently available PET tracers are more specifically designed to target tumor biology, which

potentially limit their broad applications toward normal tissue toxicity. In contrast, functional information from MR imaging, especially abundant contrast mechanisms to explore, is more suitable to study radiation-induced damage to normal tissues, where inflammation and cytotoxic events can cause detectable MR imaging signal changes. A short summary of key studies for radiationinduced normal tissue toxicity is provided in Table 7. As stated by PERCIST (Positron Emission Tomography (PET) Response Criteria in Solid Tumors), the main goal of imaging-based early evaluation of treatment response is to modify the

Table 7 Conclusions of key function imaging studies of early treatment response and normal tissue toxicity for RT Cancer Type Imaging

Modality

Early Response

Modality

Normal Tissue Toxicity

Brain

11

1. MET uptake increased significantly in glioma but not in normal cells. Treatment plan based on GTV defined by the combination of MET-PET, CT, and MR imaging improved survival in comparison to those with CT and MR imaging alone.

18

F-FDG: decreased 18F-FDG uptake in brain region receiving dose >40 Gy and correlate significantly with Wisconsin Card score. 2. 18F-DOPA: differentiate recurrent or metastatic brain tumors from late or delayed radiation induced brain injury.97 1. DWI/DTI: Decrease of FA after radiation, that is, decreased WM integrity, is dose dependent.100 WM injury quantified by DTI-derived parameters correlates with maximum dose received, suggesting WMs are serial structures in terms of dose response.101 Decreased FA after CNS radiation of pediatric patients correlate with IQ.102 2. DCE-MR imaging: changes in vascular volume and blood–brain barrier permeability during radiation are dose dependent and correlate with late verbal learning score.103 18 F-FDG: pretreatment parotid SUV weighted by parotid dose can predict posttreatment changes in parotid 18FFDG uptake and therefore predict the severity of radiation-induced xerostomia. Posttreatment signal of MR sialography for submandibular and parotid gland significantly correlated with severity of xerostomia.107

PET

C-MET95 PET96

MR imaging DWI98 DCE-MR imaging99

PET

18

F-FDG104

MR imaging DCE-MR76,77 DWI106

1. DWI: volume of increased ADC at week DWI/DTI 3 is the strongest predictor for survival DCE-MR imaging of high-grade glioma. 2. DCE-MR imaging: subvolumes of tumor with high cerebral blood volume might be radioresistant and might be benefit from adaptive radiotherapy.

18 18 F-FDG: Primarily used for posttreatF-FDG105 ment evaluation. Limited studies suggested that early response evaluation can differentiate responders/nonresponder, but prediction power is not in par with posttreatment evaluation. 1. DCE: subvolume of tumor with consisMR sialography tent low blood volume during radiation is predictive for local failure. 2. DWI: increases in mean ADC as early as 3 wk into therapy in patients with a favorable outcome.

1.

1.

18

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Head and neck

F-FDG, 18F-DOPA

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Table 7 (continued )

Cancer Type Imaging

Modality

Early Response

Esophageal

18

A multicenter prospective study suggested 18F-FDG109 pretreatment PET has better predictive power compared with PET acquired 21 d into treatment. 1. MR imaging, in general, is difficult for Delayed esophageal owing to breathing and contrast-enhanced cardiac motions. Fast imaging techMR imaging niques are preferred. 2. DCE-MR: Results from a limited number of studies suggested that significantly decreased Ktrans postchemoradiotherapy indicate good response. 18 1.18F-FDG: Multiple studies have F-FDG116,117 demonstrated the 18F-FDG during treatment is good predictor of late clinical outcome. However, time for early response estimation is important. 2.FLT: decreased FLT during early treatment is a good indicator for longterm clinical outcome. So far, no MR imaging was applied for DCE-MR response of RT. However, DWI has been Hyperpolarized 3He MR imaging applied for early response of chemotherapy and found that increased ADC during treatment identified good and poor prognostic groups.

PET

F-FDG108

MR imaging DCE-MR imaging110

Lung

PET

18

F-FDG112–114 FLT115

MR imaging NA

Modality

Normal Tissue Toxicity Increased uptake of 18F-FDG in myocardium can be related to radiation induced myocardial damage. Late contrast enhancement was found in myocardial and subendocardial layers in patients receiving RT of esophageal cancer, indicating myocardial fibrosis. Damage level is dose level dependent.111

Increased 18F-FDG uptake after radiation correlates with radiation-induced lung toxicity.

1. DCE: distinguish acute pneumonitis from late fibrosis.118,119 2. Hyperpolarized 3He MR imaging quantifies lung ventilation. Avoid high signal region in 3He MR imaging can help to mitigate radiation-induced lung toxicity.120

Liver

PET

NA

MR imaging DCE-MR123 DWI124

Cervical

PET

18

F-FDG126

NA

No studies available

DCE-MR124 DWI125

1. DCE-MR: portal venous perfusion is a surrogate image marker for normal liver function. 2. DWI: a small study suggested that ADC decreased at region receiving dose higher than 8 Gy.

18

F-FDG127,128 FLT

1. Radiation injury to pelvic bone marrow is the major concern for cervical radiation. 2. Both 18F-FDG and FLT help to differentiate active bone marrow and nonactive bone marrow to guide dose sparing.

NA

No studies available.

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MR imaging DCE-MR74,129 DWI130,131

No studies. However, 2-18F-fluoro-2deoxygalactose was developed as specific tracer for primary liver cancer and tested in animal121 and a small-scale human study.122 1. DCE-MR: hepatic arterial perfusion during treatment is a good predictor for responders and nonresponders. 2. DWI: ADC increased in high-dose region and was dose dependent. Greater increase of ADC increased likelihood of response. 1. Commonly accepted standard is 18F-FDG PET/CT 3 mo after treatment. 2. A study suggested pretreatment and week 4 of treatment represent the best time points for prediction of response. Further verification from clinical trial is needed. 1. DCE-MR: has prognostic and predictive of RT outcome. Better perfused tumor associated with overall survival. 2. DWI: ADC values increased robustly after 2 weeks of chemoradiotherapy. Early ADC changes significantly correlated with tumor volume response at the end of RT.

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Table 7 (continued )

Cancer Type Imaging

Modality

Rectal

18

PET

F-FDG

132

MR imaging DWI133

Prostate

PET

NA

MR imaging DCE-MR DWI T2-weighted

Early Response 18

Early evaluation using F-FDG at 1 and 2 wk of preoperative chemoradiotherapy is predictive overall response for patients with locally advanced rectal cancer. Percentage of increase in ADC during chemoradiotherapy is significantly greater in responders than nonresponders. Increase of ADC negatively correlates with tumor regression grade. No studies. However, Ga-PSMA PET gains interests in terms of its potentials in prostate detections. A recent study also show the feasibility of using Ga-PSMA PET for treatment response after 223Ra therapy.134 1. Both DCE and DWI are important part of multiparametric MR imaging protocol for prostate detection.135,136 2. Limited evidences showed that significant increase of ADC and decrease in T2weighted MR imaging can be detected as early as 2 wk during treatment. Need validation from further studies.135

Modality

Normal Tissue Toxicity

NA

No studies available.

NA

No studies available.

NA

No studies available.

Multiparametric MR imaging

MR imaging-guided contouring and vessel-sparing for RT improves sexual outcome for long-term survivors.137

Abbreviations: ADC, apparent diffusion coefficient; CNS, central nervous system; CT, computed tomography; DCE-MR imaging, dynamic contrast-enhanced MR imaging; DTI, diffusion tensor MR imaging; DWI, diffusion-weighted imaging; FA, fractional anisotropy; 18F-FDG, 18F-2-deoxy-D-glucose; 18F-DOPA, 18F-L-dihydroxyphenylalanine; FLT, 18F-fluoro-3’-deoxythymidine; GTV, gross tumor volume; MET, 11C-methionine; NA, not applicable; PSMA, prostate-specific membrane antigen; RT, radiation therapy; SUV, standardized uptake volume; WM, white matter.

PET/MR Hybrid Imaging into Radiation Therapy treatment course during treatment to improve treatment outcome. The active research area of adaptive radiotherapy reflects this strategy. Adaptive radiotherapy represents a series of adjustments to the radiation treatment plan delivered to a patient during the course of radiotherapy to account for temporal changes in anatomy, such as changes in tumor volume and weight loss observed on daily or periodic portal or cone beam CT imaging inside treatment room, and for changes in tumor biology or function, such as hypoxia and cellular density from longitudinal functional MR imaging or PET imaging during treatment. A schematic workflow of a typical adaptive radiotherapy is illustrated in Fig. 16. Compared with the conventional workflow (see Fig. 1), functional imaging during treatment plays a key role for adaptive radiotherapy. With evidence of both tumor response and normal tissue injury from image, the clinical decision of image-guided dose escalation or deescalation is then carried out for responsive or partially responsive tumors, through dose painting techniques in the assistance of advanced treatment delivery techniques, including IMRT/VMAT and imaging guided delivery (the workflow highlighted in green color in Fig. 16). It is worth noting that, if hypoxia imaging is used for early treatment response, hypoxia image– guided dose painting as discussed in a previous section is essentially a subtype of adaptive radiotherapy. For nonresponsive tumors, clinical decision on alternative treatments, such as adjuvant chemotherapy, hormone therapy, and prescription of radiosensitizer or salvage therapy, can be made to improve the chance of effective outcome (the workflow highlighted in red color in see Fig. 16). From treatment assessment to adaptive radiotherapy, this process involves a sequence of complicated tasks, especially (1) how quantitative measures from functional imaging modalities are

used from assessment of both tumor response and normal tissue toxicity and (2) how assessment results are integrated into adaptive radiotherapy to improve treatment overcome. A series studies for RT of liver cancer using DCE-MR imaging from the research team at the University of Michigan provide excellent examples to illustrate how these tasks are implemented for adaptive radiotherapy and, thus, will be briefly discussed here. Unresectable primary and metastatic hepatic tumors can be treated effectively with high-dose conformal RT.138 However, radiation-induced liver disease associated with the high dose can be fatal.139 The liver has one of most abundant blood vessel networks in the digestive system. Primary liver tumors are mainly supplied by hepatic arteries, whereas normal liver tissues are supported by portal veins. Therefore, quantitative measures from DCE-MR imaging, such as longitudinal changes in blood volumes of hepatic arteries and portal veins through the course of RT, can be image biomarkers for tumor response and normal liver toxicity, respectively. In a study of early evaluation of tumor response,123 DCE-MR imaging data were acquired before and during radiation treatment (after 60% of prescribed dose delivered) for 24 tumors. Voxelwise changes in hepatic arterial blood volumes were calculated and subvolumes of elevated arterial perfusion within the target volume were identified with a fuzzy clustering-based approach.140 Subvolumes of increased arterial perfusion decreased during the treatment for responsive tumors in contrast to continuous increase of hyperperfused subvolumes for progressive tumors (Fig. 17). This study demonstrated that percentage of changes in hyperperfused subvolume during early treatment is a prognostic factor for tumor progression. Dose escalation to hyperperfused subvolumes based DCE-MR imaging can improve outcomes potentially.

Fig. 16. Workflow for adaptive radiation therapy based on early evaluation of treatment response using function information from PET and MR imaging. RT, radiation therapy. (Modified from oral presentation of Dr. Brian Ross, Department of Radiology, University of Michigan, Ann Arbor, MI.)

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Fig. 17. Early evaluation of tumor response based on hepatic arterial perfusion from dynamic contrast-enhancedMR imaging with examples of hepatic arterial high perfusion probability maps of a responsive tumor (top row) and a progressive tumor (bottom row) before radiation therapy (RT; left column) and at the midpoint in RT (right column). The white curves contoured the gross tumor volumes. Reduced volume of high hepatic arterial perfusion in mid-RT was observed within responsive tumor, and an increased volume of high perfusion in mid-RT was seen with progressive tumor. (Adapted from Wang H, Farjam R, Feng M, et al. Arterial perfusion imaging-defined subvolume of intrahepatic cancer. Int J Radiat Oncol Biol Phys 2014;89:172; with permission.)

Radiation-induced normal liver damage was evaluated with longitudinal portal venous perfusion from DCE-MR imaging acquired before, at the midpoint of treatment, and at 1 month posttreatment.124 This study showed that reduction of regional venous perfusion at 1 month posttreatment correlated with the dose delivered. In addition, local venous perfusion during treatment is a significant predictor for venous perfusion 1 month after radiation. Except for high-dose regions, liver perfusion recovered after RT at liver regions where pretreatment venous perfusion was relatively low, indicating improvement of liver function after radiation treatment of hepatic primary tumor (Fig. 18). Liver portal venous perfusion results significantly correlated with clearance rate of the indocyanine green dye, a cyanine dye commonly used for clinical quantification of overall hepatic function. Therefore, portal venous perfusion from DCE-MR imaging can be an imaging surrogate for liver function and can be integrated as a dose constraint for treatment planning. To achieve more quantitative guidance for DCEMR imaging–guided adaptive radiotherapy, a global and local liver function model was further developed141 to take into account intratumor heterogeneity of normal liver function by incorporate both spatial extension and the level of perfusion changes within functional subvolumes. This liver function provides a quantitative measure of

radiosensitivity of normal liver tissue with respect to radiation dose. With normal liver radiosensitivity provided by the liver function model derived from portal venous perfusion and tumor response information from arterial perfusion, a treatment planning study142 was then carried out to verify the feasibility of adaptive radiotherapy treatment planning. This study demonstrated that, in general, the therapeutic ratio can be improved while minimizing the risk of toxicity. The degree of benefit also varied with perfusion pattern. Although promising, broad clinical implementations of adaptive therapy are hindered by several technical challenges and practical factors. First, robust quantification of tumor response from functional imaging modalities are needed; in particular, a boost volume or adjusted target volume is usually required for treatment planning of adaptive therapy. Many studies to date have adapted an empirically determined threshold value for response evaluation, partly owing to difficulties in verifying the validity of a specific threshold value through clinical data. There are several exploratory studies to develop more statistically robust selection of threshold values. For example, a functional diffusion map was created to quantify tumor response to RT during treatment of brain tumor.98 The scatter plot of ADC values within the target volume before and during RT were generated (Fig. 19). A

PET/MR Hybrid Imaging into Radiation Therapy

Fig. 18. Portal venous perfusion from dynamic contrast-enhanced-MR imaging quantifies radiation-induced normal liver toxicity. Color-coded portal venous perfusion images overlaid on coregistered treatment planning computed tomography scan before radiation therapy (RT; left) and 1 month after RT (middle) in patients 10 (top) and 12 (bottom). For each patient, the images obtained before and after RT are windowed identically, and color bars on the left side indicate perfusion in a unit per mL per 100 g per minute). Hypoperfusion before RT and reperfusion after RT were observed in patient no 10, which was associated with an improvement in overall liver function after RT. Reperfusion in the left lobe after RT was observed in patient no. 12. Perfusion dose response functions of the 2 patients are plotted in the panels at right. Isodose curves 5 10, 20, 30, 40, 50, and 55 Gy for red, green, blue, cyan, pink, and yellow, respectively. (From Cao Y, Wang H, Johnson TD, et al. Prediction of Liver Function by Using Magnetic Resonance-based Portal Venous Perfusion Imaging. Int J Radiat Oncol Biol Phys 2013;85:261; with permission.)

linear regression model fit between ADC values of the contralateral normal brain (>50 mL) before and during treatment was performed and 95% confidence intervals from the fitted model of normal tissues were then applied to the scatter plots of the target volume (ie, the dashed lines on the plots of Fig. 19). Tumor voxels were then objectively segmented into 3 different categories based on these confidence intervals from normal tissues: red, blue, and green voxels for which ADC increased significantly, decrease significantly, and did not change during treatment, respectively. Its feasibility as an early biomarker for overall survival of high-grade glioma has been validated sequentially in clinical applications.143,144 Other more model-intensive approaches, such as fuzzy clustering methods,123,140 also show promise for clinical applications. Second, owing to the complexity of tumor biology, information from a single functional modality, as has been adopted for most clinical studies to date, might not characterize tumor response comprehensively. A multiparametric approach could potentially improve the efficacy of image-guided adaptive therapy, as suggested by a recent phase I clinical trial study of concurrent

chemoradiotherapy for patients with stage III/IV head and neck cancers.145 In this study, dose escalation of chemotherapy during the treatment was performed based on the combined information from PET hypoxia, proliferation, and CT perfusion imaging (Fig. 20). Hybrid PET/MR imaging can contribute substantially in this direction, with improved spatial alignment among multiple modalities and with true synchronized capturing of multiple physiologic processes. In addition, there is no consistent consensus about the time window and frequency of imaging for early response assessment, partially owing to a lack of unified and comprehensive understanding of the underlying radiobiology. Tumor response to treatment can be time sensitive and spatially heterogeneous. For example, a study from van Baardwijk and colleagues114 suggested that, for nonresponding non–small cell lung cancers, the maximum standardized uptake value of 18F-FDGPET actually increased during the first week of treatment and decreased afterward. Last, several practical factors need to be planned carefully and orchestrated among different members of the RT team to achieve effective and efficient implementation of image-based adaptive

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Fig. 19. Images shown are at 3 weeks into a 7-week fractionated radiation. Regions of interest were drawn for each tumor by using anatomic images. (A, C, E) Regional spatial distribution of apparent diffusion coefficient (ADC) changes as color overlays for the progressive disease, stable disease, and partially responsive patients, respectively. The red pixels indicate areas of increased diffusion, whereas the blue and green pixels indicate regions of decreased and unchanged ADC, respectively. The scatter plots (B, D, F) show quantitatively the distribution of ADC changes for the entire 3-dimensional tumor volume for each corresponding patient (A, C, E), respectively. (From Moffat BA, Chenevert TL, Lawrence TS, et al. Functional diffusion map: A noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A 2005;102:5527; with permission.)

radiotherapy. We refer readers to excellent reviews146 for more details about implementing adaptive radiotherapy clinically. A typical workflow of a phase II clinical trial of image-based adaptive therapy is illustrated here as an example (Fig. 21). Besides coordination of new image acquisition, there are multiple consecutive steps in the workflow, including timely quantitative image evaluation, image-based new target delineation, decision making on replanning or continuing current treatment, replanning if chosen, and plan quality assurance. All these steps have to be done in a timely but accurate manner for adaptive radiotherapy to be successful. Sufficient training of team members and improved high-performance computing, as well as detailed written procedures and policies can contribute to further improvement of clinical adaptation of adaptive therapy.

PET/MR IMAGING FOR MR IMAGING-BASED RADIATION TREATMENT PLANNING Another application of a hybrid PET/MR imaging system, which can maximize its capacity and further improve efficiency for diagnostic and treatment planning workflow for RT is to integrate

PET/MR imaging for MR imaging-based treatment planning. There is substantial amount of efforts in the RT field2,147–150 toward MR imaging-based simulation and treatment planning, where MR imaging images, instead of CT, are acquired as the virtual simulation data for treatment planning. Tissue classification based on MR imaging simulation data will then be performed to create a synthetic CT data by assigning electron density values of known tissue types. These synthetic CT data will be used for treatment planning. Creating synthetic CT based on MR images is the key step for MR imaging-based treatment planning. Technical principles and methodologies for MR-based synthetic CT share similarities or overlap with those used for MR-based attenuation corrections for hybrid PET/MR imaging. We encourage readers to refer to the Yasheng Chen and Hongyu An’s article, “Attenuation Correction of PET/MR Imaging,” in this issue, and several excellent review articles151–154 on the topic of MR-based attenuation corrections for detailed descriptions. A short summary of MR-based synthetic CT in radiation oncology is provided. We then emphasize key differences between MR imaging acquisitions for radiation treatment planning and for diagnostic

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Fig. 20. An example of a multiparametric imaging approach, CuATSM-PET, FLT-PET, and dynamic contrastenhanced computed tomography (DCE-CT) for early treatment response evaluation of head and neck cancer. (Left column) Before bevacizumab monotherapy (time point 1), (middle column) after 3 weeks of bevacizumab (time point 2), and (right column) after 1 to 2 weeks of chemoradiation therapy (time point 3). Measurable reductions in hypoxia, proliferation, and enhancement on CT scanning were observed in response to bevacizumab monotherapy and combined therapy. This patient achieved a complete response as shown by FLT-PET and CuATSM-PET after bevacizumab monotherapy and remains disease free at 53 months. (From Nyflot MJ, Kruser TJ, Traynor AM, et al. Phase 1 trial of bevacizumab with concurrent chemoradiation therapy for squamous cell carcinoma of the head and neck with exploratory functional imaging of tumor hypoxia, proliferation, and perfusion. Int J Radiat Oncol Biol Phys 2015;91:942–51; with permission.)

PET/MR. These key differences have important impacts on the accuracy and precision required in RT. We conclude this section with an overview of the current research toward the integration of diagnostic PET/MR imaging system for MR imaging simulation of radiation treatment planning.

MR Imaging-Based Treatment Planning Radiation treatment planning relies on electron density information of the human body to calculate

the virtual dose distribution in treatment plans. Detailed information of electron density of tissues in vivo has long been measured and published.155 If the tissue type of each image voxel from MR imaging images can be identified, the electron density values of each voxel can be assigned according the known data to generate the synthetic data. Therefore, creating a synthetic CT data from MR imaging data is essentially a classification technique and most published approaches can be roughly categorized into 2

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Fig. 21. (A) Apply dynamic contrast-enhanced-MR imaging (DCE-MRI) to evaluate treatment response of radiation therapy for head and neck cancer. Blood volume (BV) maps are color coded and overlaid on post Gd T1weighted images. White contours, primary gross tumor volume (GTV); blue color, the subvolumes of the GTV with low BV. (Left) A local failure case with persistent big subvolumes of low BV at week 2. (Right) A local control case with small and reduced subvolumes of low BV at week 2. (B). The schematic flowchart of a clinical trial of adaptive therapy based on results of early treatment response evaluation from A. IMRT, intensity-modulated radiation therapy. (Courtesy of Dr Yue Cao and Dr Avraham Eisbruch, Department of Radiation Oncology, University of Michigan, Ann Arbor, MI. From Wang P, Popovtzer A, Eisbruch A, et al. An approach to identify, from DCE MRI, significant subvolumes of tumors related to outcomes in advanced head-and-neck cancer. Med Phys 2012;39:5277–85; with permission.)

types: atlas- or template-based classification156 and multiparametric MR imaging-based spectral classification.157,158 To date, the most successful application of this approach is for MR imagingbased synthetic CT for the brain. A comparative study159 of 12 patients showed there were no differences in terms of target coverage and dose limits to OARs between VMAT plans based on CT data and the synthetic CT from MR imaging. The largest dose difference, 0.2%, was observed at the maximum dose to the target volume and is of no clinical significance. There are both pros and cons of each approaches that requires careful consideration before choosing the right one for specific clinical applications. The

atlas- or template-based approaches are straightforward, relative easy to implement, and many available tools in the medical image processing field can be integrated into this workflow with minimal modifications. Owing to these advantages, commercial products, such as the MRCRT package from Philips, have adapted this approach for clinical applications. However, the accuracy of this approach largely relies on the accuracy of image registration. Image processing that minimizes intersubject anatomic variations, such as smoothing and creating a group-averaged atlas, increases uncertainties in tissue classification, especially near tissue boundaries with big electron density differences, such as bone and tissue or air and tissue.

PET/MR Hybrid Imaging into Radiation Therapy In addition, atlas-based approaches do not consider CT density changes owing to physiologic changes, which are highly subject specific and spatially heterogeneous. All these limitations with atlas-based approaches are well-addressed or avoided with the multiparametric MR imaging-based spectral classification approach. However, the spectral classification approach requires much longer MR imaging acquisitions. Elongated scanning time could be problematic for regions with large organ motions and air mobility during scan, such as the pelvis. As a result, a recent study of synthetic CT for pelvic regions160 developed a bone shape model in addition to multiparametric MR imaging acquisitions. This bone shape model is based on a principal component analysis of an existing CT atlas to address the misclassification between pelvic bones and surrounding air pockets in small bowel and the rectum that potentially move constantly during imaging. To date, MR imaging-based treatment planning has been successfully applied to the brain and the prostate. The feasibility on head and neck and the pelvis are currently active research topics with promising results. MR imaging-based treatment planning for thoracic tumors remains challenging areas owing to large air cavities.

Differences Between MR Imaging for Radiology and Radiation Oncology Geometric accuracy and repeatability of patient positioning are 2 essential requirements for simulation of radiation treatment planning. These requirements demand special considerations while using a hybrid PET/MR imaging system that is designed initially for diagnostic purposes to acquire qualified MR imaging images for radiation treatment planning. These considerations include modified MR imaging protocols and dedicated patient setup for therapeutic purposes. Several key differences in imaging setup and MR imaging protocols between MR imaging acquisition for diagnostic and therapeutic purposes are summarized in Table 8.

Current Implementation and Challenges Beside factors related to the MR imaging side discussed in Table 8, additional evaluations related to the PET side need to be done while implementing MR imaging-based simulation on a hybrid PET/MR imaging. The influence of patient immobilization devices and the rigid table top on the attenuation of PET need to be quantified. Ideally, materials and the configuration of these RT-specific devices should have small attenuation

to low-energy photons from PET tracers and should have little RF interference with MR imaging. For specific flexible MR imaging coils, its attenuation to PET detection, and its reproducibility of positioning among different patients, as well as MR image quality acquired with these coils should be evaluated and quantified. In addition, a short bore design is usually adapted for a hybrid PET/ MR imaging owing to the physical size of the PET detector array. Instead of acquiring MR images with a static table position, it is highly possible that scanning with continuous table motion will be used. Geometric distortion related to this type of acquisition need to be quantified carefully as well. Preliminary studies161,162 have been conducted to adapt a clinical hybrid PET/MR imaging, the Siemens 3T Biograph PET/MR system, for MR imaging acquisition for radiation treatment planning with specific RT components. These RT components include (1) a flat rigid tabletop with patient positioning indexing system (Fig. 22B). A plastic sandwich structure filled with a foam core replaces electrically conducting carbon fiber to avoid image artifacts in MR imaging. (2) A 6-channel flexible RF body receiver coil (see Fig. 22B) is used for imaging of brain and head and neck. A flexible RF body matrix receiver coil along with the body matrix coil embedded in the patient table are used for scans of the thorax, abdomen (see Fig. 22C), and extremities (see Fig. 22D). (3) An RF coil holder (see Fig. 22B) is designed to work with the flexible coils for brain and head/neck scan to form a C-shape configuration. Such a design not only allows additional RT-specific devices, such as a face mask (see Fig. 22A), but also allows fast and reproducible coil placements. A body coil bridge is also equipped (the black pieces connecting coils with the table on Fig. 22C and D) to aid in coil stabilization and reproducible coil placement. CT images of each RT component were acquired and included in the library of a customized software, the m-generator, which automatically integrates CT attenuation maps of these RT components into the attenuation correction software of the PET/MR imaging system according to the realtime selection of RT components and their relative positions with respect to the patient couch for each patient. Preliminary results from repeated scans of a PET/MR imaging phantom as well as 3 patients have shown that all test devices are compatible with PET/MR imaging with no visible artifacts or degradation of PET imaging quality. Repositioning accuracy for all RT components are better than 2 mm. After attenuation correction of RT components, an underestimation of the maximum

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Table 8 Comparisons between MR imaging for diagnostic radiology and for radiation treatment planning Parameters

Diagnostic Radiology

Radiation Treatment Planning

Purpose

Detection, characterization, and staging of disease 1. Patient comfort-oriented setup 2. Soft and cushioned table top 3. Decision on patient position and the use of patient supports are based on whether improving patient comfortableness and reducing patient motion or not Dedicated coils with coil geometry conformal to body shape to improve coil sensitivity and SNR

Determination of true 3D disease extent and position relative to OARs 1. Reproducibility-oriented setup 2. Rigid table top with the same patient position and immobilization devices as what will be used for treatment 3. Additional patient positioning devices such as the LAP laser for labeling the radiation isocenter Flexible coil geometry and design to accommodate special immobilization devices being put between patient and coils (see Fig. 22A–D) 1. Less than 2 mm in all planes over the volume of interest 2. High fidelity in geometry has higher priority over applicability of fast imaging

Setup

Coils configuration

Geometric distortion

1. Tolerated as long as diagnostic capability not affected 2. Trade-off between fast imaging and tolerable geometric distortions Intensity Tolerated as long as diagnostic nonuniformity capability not affected

FOV/volume coverage

Slice thickness, gap

Readout bandwidth Breath holds

QA/QC

Essential for (1) image registration required by atlas-based approach for synthetic CT, and for (2) multiparametric MR imagingbased tissue classification approach OK with reduced FOV Need to cover sufficient body volume so that no missing body part exist along the pathways of any possible beam arrangement for treatment planning 1. OK with slice gaps of 0-2 mm, slice 1. No slice gap is allowed to get accurate thickness 2-5 mm calculation of dose distribution 2. Trade-off between achievable 2. Best achievable image resolution image resolution and fast imaging Trade-off between SNR and As high as possible to reduce chemical shift minimize susceptibility and and susceptibility artifacts chemical shift At end of the inspiration to Have to match with breath hold patterns maximize the ability of patient to being used in gated radiation therapy, for hold breath and minimize motion example, at the end of inspiration for lung artifacts to increase physical separation between target and OARs, at the end of expiration for liver for ease of breath holds QA with ACR phantom and follow 1. QA with ACR phantom and follow ACR ACR guide line is usually sufficient guide line should be the first line QA 2. Additional QA with special QA phantom designed to quantify gradient linearity and B0 homogeneity are recommended; highorder correction for gradient nonlinearity should be used for acquired MR imaging image to minimize geometric distortions

Abbreviations: 3D, 3-dimensional; ACR, American College of Radiology; CT, computed tomography; FOV, field of view; OAR, organ at risk; QA/QC, quality assurance/quality control; SNR, signal to noise ratio. Adapted from Paulson E, Erickson B, Schultz C, et al. Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. Med Phys 2015;42:31; with permission.

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Fig. 22. Radiation therapy–specific components for acquiring MR images for radiation treatment planning on a hybrid PET/MR imaging system, including (A) immobilization devices, such as a face mask, (B) a radiofrequency coil holder and the flexible body receiver coil for brain and head and neck tumor imaging, (C, D) the flexible body matrix receiver coil with a body coil bridge for thoracic, abdominal and extremity imaging, (E) a schematic workflow for MR-based radiation treatment planning with acquisition at a PET/MR imaging compared with conventional computed tomography (CT)-based treatment planning (included in the dashed frame) with PET or MR imaging assisted target delineation. RT, radiation therapy. (Adapted from Paulus DH, Oehmigen M, Grueneisen J, et al. Whole-body hybrid imaging concept for the integration of PET/MR into radiation therapy treatment planning. Phys Med Biol 2016;61:3504–20; with permission.)

standardized uptake value was only 2.4%, compared with those acquired at a PET/CT scanner. It is also worth noting that an approximately 25% decrease in the signal-to-noise ratio was observed with MR imaging images using the flexible body coil compared with those acquired with a dedicated diagnostic H/N coil. In addition, continuous acquisitions of MR imaging with a

moving table163 show that geometric distortion and blurring increased along the slice selection direction. Also, distortions were speed dependent. The best geometric accuracy was achieved at a table speed of 1.1 mm/s. All these preliminary and proof-of-concept results are encouraging, suggesting that it is reasonable and practical to acquire MR images for

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Zhu et al radiation treatment planning on a hybrid PET/MR imaging system. MR imaging-based radiation treatment planning with acquisition at a PET/MR imaging system has unique advantages over traditional CT-based treatment planning with PET or MR imaging–assisted target delineation where PET or MR imaging are acquired separately (see Fig. 22E). In the traditional workflow, it requires multiple stops of the patient to get individual modalities acquired and inaccuracy in image registration will introduce uncertainties in target delineation and reduce the detectability of small lesions. With MR imaging-based radiation treatment planning on a hybrid PET/MR imaging system, it is expected to have much improved efficiency through the diagnosis and treatment planning workflow for RT. With further optimization of workflow, improvement in both MR imagingbased attenuation correction for PET and in MR imaging-based synthetic CT, simultaneous PET/ MR imaging acquisition will also minimize misregistration between different modalities and will improve detectability for tumor heterogeneity. It is worth emphasizing that, without thorough tests of geometric distortion, image resolution and intensity uniformity of the acquired images, MR imaging protocols optimized for diagnostic applications should not been used directly for radiation treatment planning. Geometric distortion in MR imaging are mainly contributed by system-level geometric accuracy, such as field inhomogeneity and gradient nonlinearity, and by patient-induced spatial distortion, such as susceptibility and chemical shift artifacts. Optimization of MR imaging protocols should be one of essential tasks for implementation MR imaging simulation on a hybrid PET/MR imaging system, and it requires close collaborations among MR imaging physicists, medical physicists, radiologists, and radiation oncologists. MR imaging protocols should be optimized for individual body parts and also should take reproducibility of patient position into consideration. A good example of MR imaging protocols for radiation treatment planning can be found in an excellent study by Paulson and colleagues.164 Optimization of PET/MR imaging workflow is also an area requiring further studies and investigation.165,166 System-level geometric accuracy should be monitored closely and a tighter standard than those commonly used in diagnostic MR imaging for gradient nonlinearity and field inhomogeneity should be implemented for MR imagingbased radiation treatment planning. For example, the use of a dedicated geometric distortion phantom167 along with high-order nonlinearity correction algorithms should be integrated into the workflow of MR imaging-based radiation treatment planning.

Last, when implementing a hybrid PET/MR imaging scanner for MR imaging-based RT, the physical size of the magnet bore of the current system should be considered when planning MR imaging simulations. Different from diagnostic imaging, immobilization devices have to be used for some patients to maximize the reproducibility of patient position throughout the treatment. The use of some devices, such as the slant board and vacuum bags, will exceed the limit of magnet bore. Therefore, collaboration and coordination with the RT team, well-documented policies and procedures, as well as sufficient training of team members will be the key factors of success.

PET/MR IMAGING FOR RADIATION THERAPY: CHALLENGES AND FUTURE DIRECTIONS Without exaggeration, modern RT is image-based and image-guided RT. Biological and functional information from PET/MR imaging further provides unprecedented opportunities to advance RT toward more precise and more personalized treatments with improved therapeutic ratio and improved quality of life of patients after treatment. Many clinical advances heavily rely on the accuracy and precision of imaging information, for example, the uptake level of hypoxia PET directly determines spatial varying dose escalation for dose painting by number. Any imaging uncertainty, including artifacts, will propagate through the whole process of imaging-guided RT and further complicate the already complex understanding of tumor biology and radiobiology. Continuous minimizing of uncertainty is one of the most important challenges for the further integration of PET/MR imaging into RT. In general, there are 3 major sources of uncertainty: imaging specific, system specific, and process specific. Establishing a rigorous quality assurance and quality control procedure and optimization of dedicated imaging protocols for RT are among essential steps for broadening applications of PET/MR imaging for RT.

Imaging-Specific Uncertainty For PET, one of most important uncertainties that impact RT is the intrinsic low image resolution, which limits PET interpolation of small lesions and intratumor heterogeneity (see Fig. 5). With simultaneous PET/MR imaging, it is expected to advance research and clinical implementation of MR-based partial volume correction for PET.168 Superb soft tissue contrasts provided by MR imaging have clear advantages over CT-based correction approaches in terms of modeling mixed tissues within each image voxel of a PET image for

PET/MR Hybrid Imaging into Radiation Therapy partial volume correction. In addition, simultaneous acquisition of PET/MR imaging will minimize additional steps of image registration and resultant smoothing/blurring, which were proven to be problematic for previous MR imaging-based correction approach with separately acquired PET and MR imaging. With simultaneous PET/ MR imaging and high-performance computing techniques, it is reasonable to expect the integration of MR imaging-based partial volume correction with online PET reconstruction in the near future. Improvement on partial volume correction will also boost MR imaging-based computation of the arterial input function for PET kinetic modeling.169 Preclinical studies170 have demonstrated the feasibility of converting the arterial input function of Gd-MR imaging to those of 18FFDG and the 18F-fluoroethyl-L-tyrosine. Advance of knowledge in these areas will, in principle, alleviate the uncertainty associated with the intrinsic low resolution of PET imaging. For MR imaging, especially with advanced sequences like DCE and DWI, a trade-off between overall image quality and the requirement of fast imaging has to be evaluated carefully. Because diffusion MR imaging measures microscopic motion in vivo, ultrafast imaging techniques, usually echo planar imaging MR imaging, are used for image acquisition to eliminate bulk body motions. Geometric distortions from susceptibility and eddy current artifacts, which are intrinsic to echo planar imaging sequences, may potentially lead to uncertainty in target definition (Fig. 23, middle). Remedies including phase map-based distortion correction and using spin echo-based PROPELLER sequence171 can improve geometric accuracy, but will increase acquisition time or potentially encounter the specific absorption rate limits, respectively. In addition, measurement of DWI and diffusion tensor MR imaging, especially the estimation of ADC, depends on the performance of the gradient system of the MR imaging scanner. A robust quality assurance procedure should be implemented periodically to reduce the variability in ADC measurement owing to system performance. There are several technical challenges for increasing clinical applications of DCE-MR imaging. The requirements of good temporal resolution typically restrain the achievable spatial resolution. An optimized DCE-MR imaging is the key for successful clinical application and individual optimizations should be carried out for different tumor types owing to differences in tumor angiogenesis and different levels of artifacts, such as body motions. In addition, robust estimation of Ktrans and ne relies on measurement accuracy in T1 and the arterial input function (AIF), which is the

main source for uncertainty in DCE-MR imaging and remains an active research topic.

System-Specific Uncertainty Fluctuations in system performance need to be monitored continuously and adjusted. This is especially for PET/MR imaging application in RT, because quantitative measures from imaging will be used for clinical decision making. For example, the assessment of treatment response will depend largely on a quantitative analysis of the uptake level of PET tracers and, therefore, rigorous calibration of PET dosimetry components of the PET/MR imaging will be essential. Gradient nonlinearity and main magnetic field inhomogeneity are the 2 most important factors that impact the geometric accuracy of acquired MR imaging for RT. In general, in addition to adopting generic and routine quality assurance and quality control procedures, which have been currently implemented in radiology, several dedicated quality assurance and quality control procedures that aim at improving the accuracy and repeatability of quantitative measures from PET/MR imaging should be implemented. For example, dedicated perfusion and diffusion MR imaging phantoms have been developed by the Quantitative Imaging Biomarker Alliance, which was initiated by the Radiological Society of North America and formed as the joint forces from research, the clinic, and industry to provide a standardized testing platform across different centers and scanners of different vendors. Many resources provided by the Quantitative Imaging Biomarker Alliance will be good references and tools for establishing quality assurance and quality control for PET/MR imaging in applications of RT.

Process-Specific Uncertainty From imaging to clinical decision making, there are many complicated processes involved, including image reconstruction, image registration both among different image modalities and among longitudinal acquisition of the same modality, and artifact correction and contouring. Any uncertainty from one process will cascade downstream to impact the overall quality of quantitative image information. For example, uncertainty in contouring owing to misregistration is the most common uncertainty that radiation oncologists have to deal with (see Fig. 23, right). A greater margin is usually taken as the countermeasure to compensate uncertainties owing to image registration or low image resolution with a drawback of increased radiation dose to surrounding normal tissues.

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Fig. 23. Examples of uncertainties in PET/MR imaging application to radiation oncology. (Left) Axial gadoliniumenhanced T1-weighted MR image of laryngeal small cell carcinoma (as reference image) shows a histologically proven second metachronous tumor in the left aspect of the tongue (arrowheads indicate actual tumor size and placement). (Middle) Registered diffusion-weighted image superimposed on the gadolinium-enhanced fat-saturated T1-weighted MR image. Owing to significant geometric distortion from susceptibility artifacts, the tumor seems to be smaller and located anteriorly (arrow). (Right) Registered PET with 18F-2-deoxy-D-glucose superimposed on the gadolinium-enhanced fat-saturated T1-weighted MR image. Because of patient motion during PET acquisition, the tumor seems to be located in the cheek (arrow). (From Varoquaux A, Rager O, Dulguerov P, et al. Diffusion-weighted and PET/MR imaging after radiation therapy for malignant head and neck tumors. Radiographics 2015;35:1502–27; with permission.)

Intrinsic spatial alignment from simultaneous acquisition of PET/MR imaging will minimize registration uncertainty. In addition, research on MR imaging-based motion correction for PET172 is of special importance for PET imaging of thoracic and abdominal tumors. If MR imaging is also used for MR imaging-based treatment planning purpose, patient setup during imaging should be what will be used during radiation treatment with proper patient immobilization devices. Contouring is considered as one of most errorprone process during RT. High intrarater and interrater variability is reported in terms of manual target volume contouring. There is increasing interest and enthusiasm in the field toward automatic or semiautomatic contouring.173,174 Algorithms for autocontouring also evolved from a simple statistical model-based approach toward intensive computation as well as potentially more robust models, such as machine learning and texture analysis. With the intrinsic advantages of simultaneous acquisitions of PET/MR imaging with multispectral information, the robustness and accuracy of automatic and semiautomatic contouring based on PET/MR imaging are expected to be improved further. Largescale, organized, multicenter studies are needed to carefully validate the feasibility and robustness of these new approaches. There are also significant efforts toward sharing resource and standardization of imaging protocols, imaging reconstruction, processing, and quantification in the field of imaging quantification. For example, National Institutes of Health-supported Quantitative Imaging Network focuses on “research and development of quantitative imaging methods for the measurement of tour response to therapies

in clinical trial setting, with overall goal of facilitating clinical decision making.” To date, 17 multidisciplinary teams participate the Quantitative Imaging Network and the research topics cover imageguided quantitative analysis of response to drug and RT, including multiple modalities of PET for metabolism and hypoxia, CT, diffusion and perfusion MR imaging, and MR spectroscopy. With all these significant efforts in the field of image quantification, we are optimistic that better solutions to minimize uncertainties will become available for broad research and clinical applications, enabling functional imaging to show its full potential.

Optimized PET/MR Imaging Workflow Efficient PET/MR imaging protocol/workflow is the key for successful implementations of PET/MR imaging for RT. Optimized clinical PET/MR protocols will have a typical clinical scan time fame of about 30 to 60 minutes for a whole body scan of 4 to 5 bed positions. Currently, most MR imaging protocols that fit into this time frame are anatomic MR imaging as well as sequences for attenuation correction of PET. Using scans on a Siemens Biograph simultaneous PET/MR imaging scanner as examples,24 a typical PET/MR imaging protocol will include a Dixon dual echo 3D Volumetric Breath Hold Examination sequence (3D Dixon VIBE)175,176 to obtain high-resolution fat and water proton images, followed by a T2-weighted halfFourier single-shot turbo spin echo sequence (t2 HASTE)177 or a 3D T2-weighted image with sampling perfection with application optimized contrasts using different flip angle Evolution (3D T2

PET/MR Hybrid Imaging into Radiation Therapy SPACE).178 Additional contrast-enhanced MR imaging scans will be added according to clinical indications. With advanced MR imaging sequences, such as DWI and DCE-MR imaging, it will add at least 5 to 10 minutes additional scan time to each bed position, which could make the scan time much longer. A recent new innovation in MR imaging acquisition, MR fingerprinting (MRF),179 can potentially provide more efficient imaging acquisitions for PET/MR imaging. Different from conventional MR imaging, where individual acquisitions of different MR imaging contrasts, such as T1 and T2, have to be acquired separately, MRF takes advantage of mixed contributions of T1 and T2 from the steady-state free precession sequence and assumes that signatures of individual contrast mechanisms will leave unique “fingerprints” in the acquired final signal that can be separated with pattern recognition or machine learning techniques during postprocessing. Therefore, using MRF, it is capable of generating multiple image contrasts with the similar scan time as what is typically used to acquire 1 conventional MR image alone. Therefore, the conserved time can be reserved for advanced MR imaging modalities. MRF applications in PET/MR imaging warrant future study. In addition, redundancies of functional information among different PET and MR imaging modalities should be evaluated thoroughly so that the best representative modalities will be identified for image acquisition with improved acquisition efficiency.

SUMMARY Hybrid PET/MR imaging, although promising, is still in early development for treatment planning. The high initial investment and maintenance costs raise questions as to whether PET/MR imaging for RT treatment planning is superior than that of PET/CT. This article briefly reviewed promising new research and clinical applications of hybrid PET/MR imaging in radiation oncology through better assessment of tumor biology and heterogeneity. With ongoing improvements in PET/MR imaging workflow, more specific PET tracers, and fast and robust MR imaging acquisition protocols, it is reasonable to expect that PET/MR imaging will play increasingly important role in better target delineation for treatment planning and will have clear advantages in the early evaluation of tumor response and in a better understanding of tumor heterogeneity. With advances in treatment delivery such as adaptive radiotherapy, and with the potential of integrating PET/MR imaging with the emerging field of research on radiomics for radiation oncology,180,181 new quantitative and

physiologic information by hybrid PET/MR imaging could lead to more precise and personalized RT with improved therapeutic outcome and better quality of life.

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