Technical prerequisites and imaging protocols for CT perfusion imaging in oncology

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Technical prerequisites and imaging protocols for CT perfusion imaging in oncology Ernst Klotz a , Ulrike Haberland a , Gerhard Glatting b , Stefan O. Schoenberg d , Christian Fink c , Ulrike Attenberger d , Thomas Henzler d,∗ a

Siemens Healthcare, Computed Tomography and Radiation Oncology, Forchheim, Germany Medical Radiation Physics/Radiation Protection, Medical Faculty Mannheim, Heidelberg University, Germany c Department of Radiology, General Hospital Celle, Celle, Germany d Institute of Clinical Radiology and Nuclear Medicine, University Medical Center, Medical Faculty Mannheim, Heidelberg University, Germany b

a r t i c l e

i n f o

Article history: Received 25 May 2015 Accepted 11 June 2015 Keywords: Perfusion CT Dynamic contrast enhanced computed tomography Functional imaging Oncology

a b s t r a c t The aim of this review article is to define the technical prerequisites of modern state-of-the-art CT perfusion imaging in oncology at reasonable dose levels. The focus is mainly on abdominal and thoracic tumor imaging, as they pose the largest challenges with respect to attenuation and patient motion. We will show that low kV dynamic scanning in conjunction with detection technology optimized for low photon fluxes has the highest impact on reducing dose independently of other choices made in the protocol selection. We discuss, derived from relatively simple first principles, on what appropriate temporal sampling and total scan duration depend on and why optimized contrast medium injection protocols are also essential in limiting dose. Finally we will examine the possibility of simultaneously extracting standard morphological and functional information from one single 4D examination as a potential enabler for a more widespread use of dynamic contrast enhanced CT in oncology. © 2015 Elsevier Ireland Ltd. All rights reserved.

1. Introduction CT perfusion imaging, or more strictly defined, dynamic contrast enhanced (DCE) imaging with CT, is gradually evolving into a useful biomarker in oncology to be used for differential diagnosis, prognostic stratification and response monitoring. Despite substantial advances over the last 20 years it has, however, not really arrived in mainstream [1]. The aim of this manuscript is to define the technical prerequisites of modern state-of-the-art 4D imaging at reasonable dose levels and discuss consequential implications on scan protocols. We will focus on the major issues present in abdominal and thoracic tumor imaging, as they pose the largest challenges with respect to attenuation and patient motion. Brain tumors are discussed in another article in this issue [2]. The manuscript is partly based on and draws from the personal experience of the authors in developing scan modes and analysis techniques for DCE-CT as well as experience in clinical routine

∗ Corresponding author at: Institute of Clinical Radiology and Nuclear Medicine University Medical Center, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Fax: +49 621 383 3817. E-mail address: [email protected] (T. Henzler).

and clinical trials on DCE-CT. Our aim is to provide a general vendor independent overview of basic factors influencing the selection of appropriate scan protocols particularly for abdominal oncology. The discussion is based on currently available high-end equipment and might be slightly biased occasionally by our own experience since some of the technological issues that are related to DCE-CT are proprietary and are not well documented within the available literature.

2. Scope and historical evolution DCE-CT imaging is based on the intra-venous injection of a short compact bolus of contrast media. The temporal distribution of this bolus is measured by repeatedly scanning the tissue of interest. The observed change of the local tracer concentration is then quantitatively described by fitting a mathematical model to the data, which is based on simplified physiological principles (indicator-dilution, tracer-kinetic modelling). We will focus on quantitative volume DCE imaging, i.e. the generation of 3D maps of functional parameters and their volume based quantitative analysis. However, there is also growing interest in 4D CT vascular imaging without quantitative analysis of 3D perfusion maps, i.e., multiphase imaging for

http://dx.doi.org/10.1016/j.ejrad.2015.06.010 0720-048X/© 2015 Elsevier Ireland Ltd. All rights reserved.

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the evaluation of aortic endoleaks as well as dynamic imaging of peripheral vessels in CT run-off studies [3]. In the technical evolution of CT there were several prerequisites that were necessary to allow DCE-CT to open the door for a more widespread clinical application. The first technical development was the introduction of continuous rotation via slip-ring technology at the end of the 1980s, which was the central technical enabler that allowed morphological volume scanning using helical CT [4]. At the same time this technology allowed repeated scanning of a typically 1 cm thick slice with a sampling rate high enough to permit quantitative modeling [5]. The number of simultaneously acquired slices rapidly increased to 4, 16 and 64 with a tremendous impact on volume coverage and attainable spatial resolution for standard scanning and particularly for CT angiography (CTA). This increase, however, had only moderate influence on DCECT imaging as the total coverage just increased to maximally 4 cm, which was still not enough to cover whole tumors and organs particularly under residual respiratory motion. Nevertheless most of the basic principles of quantitative perfusion imaging were established during this period typically using 120 kV tube voltage and a sampling rate of 1 s [6–8]. This situation changed when coverage became large enough to include the whole tumor and adjacent anatomical structures with sufficient temporal sampling at the end of the 2000 s. This was achieved either by increasing the detector width to between 8 and 16 cm or by fast periodic helical acquisitions [9,10]. Full coverage allowed a better motion correction, increased reproducibility and allowed depicting the heterogeneity of the tumor in 3D. Optimizing DCE-CT imaging is a complex problem that has to consider the clinical question of the examination, patient habitus, tumor type and location, injection and scan protocol as well as the analysis method. The substantial discordance with respect to suggested protocols and approaches to be found in the literature is mainly due to the fact that despite advances the major remaining limitation today is radiation exposure. In most studies this fundamental limitation is addressed by making compromises with respect to coverage, temporal sampling, spatial resolution, image quality, modelling complexity or combinations of any of these. Thus, comparison of quantitative results is frequently difficult. We will not try to resolve these issues, but rather aim at providing some general guidance principles. The most important one will be the benefit of low dose low kV scanning in conjunction with detection technology optimized for low photon fluxes, because this generally reduces dose by up to a factor of two. This effect is independent of the scan techniques and applies to all scan protocols and analysis methods. We will frequently refer to the consensus status and guidelines for DCE-CT in the assessment of tumor vascular support (CSG) defined in a workshop in 2010 [11]. In addition, we will elaborate on this by providing some background reasoning derived from first principles on what appropriate temporal sampling and examination times might depend on and why optimized injection protocols can also help to limit dose.

immediate success control after interventional therapy, and as a prognostic factor predicting therapy outcome. With DCE-CT imaging, depending on the selected approach, a multitude of parameters can be determined. These include blood flow (BF), blood volume (BV), flow extraction product (FE), permeability surface area product (PS), mean transit time (MTT) and the liver specific parameters arterial liver perfusion (ALP, arterial BF of the liver), portal venous liver perfusion (PVP, portal venous BF of the liver) or the hepatic perfusion index (HPI, ratio of ALP to total liver perfusion). In differential diagnosis DCE-CT showed promising results in the differentiation of lung cancer subtypes [13], lymphoma subtypes [14], differentiating diverticulitis from colorectal cancer [15] and several applications in the liver, e.g., degree of fibrosis [16], discriminating liver hemangioma from hepatocellular carcinoma [17,18] or HCC from metastatic liver tumor [19]. The capability of DCE-CT to assess early response to therapy with various anti-angiogenic and vascular targeting drugs has been shown in many single center studies. Quantitative DCE-CT parameters, especially BF and BV, proved to be effective in demonstrating hemodynamic changes during and after therapy (Table 2 in [1], Table 3 in [11,20,21]. Also with interventional treatment the change of perfusion parameters after treatment was shown to indicate success of therapy [22–24]. Studies of early response to chemo- and/or radiation therapy suggest that a high pre-treatment BF and/or BV is associated with a good response for multiple cancer types [25–33]. The predictive value of DCE-CT depends on cancer type and therapy. In lung cancer treated with conventional chemotherapy for example DCE-CT does not add to overall survival prediction [34]. Moderate results were reported by Bisdas et al. for oropharynx squamous cell carcinoma treated with induction chemotherapy [30]. On the other hand, Morsbach et al. reported excellent prognostic results for arterial perfusion measurements on survival prediction of patients with liver metastases treated with radio-embolization therapy [35]. In prognostic studies it is important to reproducibly determine accurate values for the perfusion parameters that are to be used for risk stratification. The aim is to determine absolute cut off values from outcome-oriented studies to use them later in the therapy management decision of an individual patient. Moreover, for the specific goal of follow-up imaging in early response to drugs or immediate response evaluation after interventional therapy, good reproducibility is essential. Determining the relative changes with regard to the pre-therapeutic scan is typically sufficient. Systematic differences related to scanner, scan protocol or analysis model that influence accuracy are of lesser importance in longitudinal studies, as long as identical equipment and procedures are used for the same patient. BF (ALP for the liver) and BV are the two parameters most often investigated and reported. It is worth a note that to the best of our knowledge, there are no published studies where parameters related to permeability had real additional independent predictive value.

3. Potential roles of CT perfusion imaging in oncology

4. The importance of the lowest kV possible as a general radiation reduction measure

Before going into technical details it is helpful to briefly summarize the different roles DCE-CT might play in oncology and how this potentially affects the choice of protocol and type of analysis. It is well recognized that with therapies targeting on tumor vasculature there is a need for revised RECIST criteria [12]. Especially with the broader application of vascular targeting drugs, new biomarkers are needed and DCE-CT is such a biomarker on its way to maturation [1]. Research on DCE-CT in oncology focused on differential diagnosis, therapy response assessment and monitoring,

Perfusion imaging uses mathematical modelling of the change of the iodine concentration in small tissue voxels to derive quantitative perfusion parameters. This concentration can be very small, particularly in the early phases of enhancement. Therefore any measure that increases iodine contrast is helpful; other types of tissue contrast are of lesser importance. Because of the high atomic number of Iodine (Z = 53) and the position of the k-edge (33.2 keV) photon interaction is mainly via the photoelectric effect whose

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Fig. 1. Relative iodine contrast measured in a 30 and 40 cm water-equivalent phantom for tube voltages between 70 and 120 kV on a 3rd generation dual-source CT system (SOMATOM Force, Siemens Healthcare Sector, Forchheim, Germany).

strength substantially increases with decreasing photon energy. Fig. 1 shows that iodine contrast changes by a factor of two over the tube voltage range of 70–120 kV. Because of this well-known effect 80 kV very quickly became the accepted standard for the low attenuation setting of brain perfusion imaging [36,37]. With the exception of upper neck tumors adoption of lower kV particularly of 80 kV has been slow. The CSG recommendations for instance, reflecting the consensus state of early 2010, contain a table of kV recommendations based on weight or BMI that partially recommend 100 kV for thorax, abdomen and pelvis for smaller patients, but still recommend 120 kV for abdominal perfusion imaging of patients with a BMI >25 or a weight of 75 kg (Table 6 in [11]). In order to understand these traditional cautious restrictions for situations with a higher attenuation, it is necessary to have a closer look at the whole signal chain. If kV are lowered, tube output decreases quickly (at least with the inverse square of the kV) and overall attenuation increases somewhat because of the softer photon spectrum. This lowers the detector signal and increases noise. In order to achieve an equal iodine contrast-to-noise (CNR) ratio at lower kV levels the tube current needs to be increased. The main benefit of low tube voltage high tube current acquisition is that it leads to a substantially reduced patient dose. Fig. 2 shows CNR measurements on a 3rd generation dual-source CT system (Somatom FORCE, Siemens Healthcare Sector, Forchheim, Germany) that indicate that patient dose for the same CNR decreases by a factor of two comparing 120–80 kV for the attenuation range of thoracic and abdominal scans. But this reduction does not come for free because of the much smaller photon flux that reaches the detector. If 120 kV and 60 mAs are used as a reference: halving the dose by using 80 kV requires increasing the mAs by 75%, but the detector signal is only about 1/3. This poses considerable challenges for the detection system. Any real world X-ray detection system by necessity contains an electronic amplification component for every channel that raises the tiny voltages generated by the scintillation–photodiode combination enough to allow analog to digital conversion. This amplification carries its own fluctuations (electronic noise) that are added to the inherent photon noise of the measured signal. For nor-

mal photon fluxes this contribution is small and is typically ignored. But for very small fluxes it will become relevant and eventually induce non-linear effects that cause visible artifacts, CT number changes, and ultimately loss of contrast. Optimized detectors with fully integrated electronics that minimize electronic noise not just have overall lower noise but also guarantee linear performance for the much smaller signals that occur in low kV imaging. Fig. 3 demonstrates this with measurements in 30 and 40 cm water phantoms performed on otherwise completely identical 2nd generation dual-source systems equipped with a conventional detector and the recently introduced fully integrated Stellar detector (Siemens Healthcare Sector, Forchheim, Germany). The fluctuations of the photon flux at the detector approximately follow a Poisson distribution with the well-known property in image space that the square of the noise is inversely proportional to the mAs with otherwise identical scan parameters: reducing the noise by a factor of 2 requires increasing the mAs by a factor of 4. Noise ␴ and mAs for an ideal system with negligible electronic noise follow a simple relationship that is used in Fig. 3: ×

√ mAs = const

Plotting this product for decreasing mAs values shows that for 30 cm water attenuation (a less than average sized patient) the conventional design can still be used at 80 kV for mAs values of not much less than 100 mAs (Fig. 3a). For 40 cm water attenuation (a large patient) 80 kV cannot be safely used anymore, electronic noise becomes relevant at 150 mAs already (Fig. 3b) and visual artifacts appear (Fig. 3c). The integrated design on the other hand has considerably more headroom and can be safely used down to 100 mAs for basically all patients. But as no real world system is completely perfect, further reduction of the signal eventually will always lead to a reduction of contrast. So when using 70 kV or mAs values of 50 and less it will still be necessary to put a limit on patient size. It can be expected that integrated detector design will quickly become an industry standard. As far as we know the detector designs of all major vendors follow similar principles on high-end CT systems. If there is uncertainty about the low kV performance of a particular system, we would recommend asking the manu-

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Fig. 2. Relative dose values (CTDIvol) necessary to achieve the same iodine contrast-to-noise-ratio. Measurements were done in a 30 and 40 cm water-equivalent phantom for tube voltages between 70 and 120 kV on a 3rd generation dual-source CT system (SOMATOM Force, Siemens Healthcare Sector, Forchheim, Germany).

Fig. 3. Noise and iodine contrast measurements on SOMATOM Definition Flash systems with a conventional detector and a detector with fully integrated electronics (Stellar). (a) Plots for a 30 cm water phantom at 80 and 100 kV at various mAs settings. The horizontal lines are plotted at the level of the value measured at the highest mAs setting for each kV and detector type. The deviation from this horizontal line below 60 mAs for the conventional detector indicates electronic noise increasingly disturbs the measurement. (b) Plots for a 40 cm water phantom at 80 and 100 kV at various mAs settings. The horizontal lines are plotted at the level of the value measured at the highest mAs setting for each kV and detector type. The general reduction of noise for the Stellar detector at high mAs values for both kV is about 14%. The 80 kV measurements for the conventional detector strongly deviate from the horizontal line already at 150 mAs, indicating that 80 kV cannot be reliably used for larger patients. (c) Images at 80 kV and 100 mAs in the 40 cm phantom show strong inhomogeneous artifacts for the conventional detector. (d) Iodine contrast at 80 kV in the 40 cm phantom show that eventually for very low mAs even the contrast resolution breaks down. The counterintuitive reduction of the at 25 mAs in b is caused by this effect.

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facturer of your system for details for the maximum patient size to be included in a study. For multicenter studies it might ultimately be advisable to perform phantom measurements such as the ones described above to be on the safe side as well as to make measurements from different CT systems comparable. 5. Sufficient coverage as a practical problem solver DCE-CT imaging relies on measuring small changes of the iodine concentration before a background that is implicitly assumed to be constant. Therefore anything that violates this assumption and introduces change that is not caused by iodine change is a serious confounding issue. Insufficient breath hold is the most prominent of these effects particularly in abdomen and thorax. Its relevance strongly increases with the heterogeneity of the tumors to be examined and the nature of the background anatomical structures. While the time attenuation curves (TACs) of homogeneous central sections of the liver look very similar, even if the voxel moves by 2 cm during the scan, this is not true anymore in the liver dome, for heterogeneous liver lesions or bronchial carcinoma against the background of the normal lung. Because of partial volume effects the error also increases in small tumors. While these effects might still be acceptable if the main aim is discrimination of the tumor from the normal background, they will definitely have considerable negative impact on quantitative reproducibility in longitudinal studies. Rigid registration techniques based on bony structures are stateof-the art for head perfusion imaging, but they are not well suited for abdominal structures that frequently also change their shape in different states during the respiratory cycle. There are efficient nonrigid registration techniques that can take care of this [38–41], but they require extra headroom in the axial direction such that at least the tumor or optimally most of the whole containing organ (e.g., liver, kidney or pancreas) is completely covered in every occurring respiratory state. Increased coverage can be achieved by either increasing the detector width (8–16 cm) or by using continuous periodic spiral techniques. Periodic spirals can be performed with the required cycle time of not more than 2 s (see next section); on a 3rd generation dual-source CT system for instance 22 cm can be scanned every 1.5 s. Analysis accuracy and noise performance is similar to nonmoving table acquisition as long as the analysis software correctly and fully takes the locally non-equidistant sampling into account [42,43]. How much coverage is minimally required depends on tumor size and organ. For the liver 8 cm are probably a minimum. The portal vein needs to be covered and a tumor of 4 cm craniocaudal extent optimally placed in the center with a respiratory motion of not more than one cm in either direction requires at least 6 cm of full quality coverage. Because of cone beam effects this is what a scan with 8 cm detector width in the isocenter provides within a 30 cm FOV (calculated assuming a focus isocenter distance of 60 cm). 6. Modelling, temporal sampling, total examination time The interdependence of sampling frequency, total scan time and the analysis model is not well understood. In conjunction with the compromises necessary to put a reasonable limit to the radiation dose this has caused substantial discordance with respect to suggested protocols in the literature. The required temporal sampling and the total duration of the examination depend on details of the analysis model and on which functional parameters are to be determined. They also depend on assumptions made about when which part of contrast media (CM) exchange (intravascular, interstitial) occurs.

Fig. 4. Impulse residue function IRF of the AATH model (see also text). F is blood flow, MTT is mean transit time, BV is the blood volume calculated from the central volume principle as BV = F × MTT, FE (or KTrans ) is the flow extraction product, E the extraction fraction, PS the permeability surface area product (calculated from E = FE/F using the Renkin–Crone equation), Hk the hematokrit and Ve the extracellular volume fraction.

The perceived importance of limiting dose to justifiable ceiling values can be seen from the CSG recommendations that suggest limiting the dose to 20 mSv for 4 cm coverage, but to only 30 mSv for wider coverage [11]. This was obviously not a really rational choice. If coverage is for instance increased to 16 cm, this can only be fulfilled by reducing the number of time points by at least a factor of two if everything else is kept constant. This unfortunately is a typical reality in defining protocols for DCE-CT; they are built backwards from a dose limit. It is generally agreed upon that DCE-CT is predominantly first pass imaging, but the definition of what first pass exactly means remains vague. This is further confounded by the fact that sometimes even statements of the kind “Contrast material stays intravascular during first pass” can be found within the available literature. There is general agreement that the sampling rate in the early phase should be high. The CSG guidelines states: “For measurements of tissue blood flow, an acquisition time of 45 s comprising the “perfusion phase” (i.e. first pass of contrast material) is advisable with a sampling interval (image cycle time) of no less frequent than one image every 2 s.” [11]. However, the CSG also state: “Longer acquisitions comprising at least six additional time points within the interstitial phase are required for analysis of the passage of contrast material into the extravascular space.” [11]. While we fully agree with the first statement, we tend to question the real importance of the second one. We believe that most of the information about the passage of CM into extravascular space is already contained in the “perfusion phase”. We will demonstrate this by “reverse deconvolution” using a model frequently used for the analysis of DCE-CT perfusion data. Deconvolution approaches assume that the tissue time concentration curve C(t) can mathematically be described as convolution of an arterial input function (AIF) CA (t) with an unknown impulse residue/response function (IRF).

t C (t) =









CA t  − t × IRF t − t  × dt 

(2)

t

The analysis determines the IRF by solving this equation and derives quantitative perfusion parameters from it. In the following we will use the AATH model (short for adiabatic approximation to the tissue homogeneity model) [37]; the shape of its IRF is graphically displayed in Fig. 4. Variants of this model have been frequently used for clinical studies because they are commercially available. The full model including determination of the arterial arrival delay t in Eq. (2) is used in FDA approved perfusion analysis tools (GE Perfusion 4.0 and Siemens syngo. via CT Body Perfusion VA30). In this form it has 6 free parameters: baseline attenuation and arrival delay t (not plotted); blood flow F, mean transit time

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Fig. 5. Simulated enhancement curves generated from a measured population AIF by convolution with various IRFs following the AATH model (see text). The dotted line is the scaled AIF. (a) Flow and blood volume are kept constant, the extraction fraction is modified. Changes affect the phase after the peak. (b) Flow extraction product and blood volume are kept constant, flow is modified. Changes affect the upslope phase until the peak. (c) Same settings as in Fig. 5a. Intravascular (red) and extravascular (purple) portion of the enhancement curve are plotted separately. The fraction of the extravascular portion at 25 s (tissue peak) and 40 s (end of first pass) is given relative to the value at 90 s.

MTT, flow extraction product FE and a decay parameter containing the extravascular distribution volume. Previous versions from both vendors either did not model arrival delay (GE Perfusion 3.0) or assumed the decay parameter to be zero (Siemens syngo VPCT Body). If we use a realistic AIF and convolve it with different IRFs, we will be able to see the major effect of parameter changes on the enhancement curves. Fig. 5 is based on a measured population AIF determined from the femoral artery of 28 patients in a prostate cancer DCE-CT study [44]. Blood volume is assumed to be constant (10 ml/100 ml); the exact value of blood volume is of lesser importance as it will mainly scale the enhancement curves. MTT is changed from 5 to 10 s, extraction fraction is changed from 0.2 to 0.4 and the relative extravascular distribution volume is assumed to be 0.3. These parameters reasonably span the range for tumors to be found in the literature. In Fig. 5a flow is kept constant and the extraction fraction is modified. It is clearly visible that the upslope phase until shortly before the peak is identical; the major differences occur in the phase after the peak until the baseline return of the AIF. In Fig. 5b the flow extraction product is kept constant and the flow is modified. Differences almost exclusively affect the upslope phase until the peak. In Fig. 5c intra- and extra-vascular contributions are separately plotted for constant flow and changing FE. It is evident that most of the transport into extravascular

space actually occurs within first pass. If the enhancement at 90 s is assumed to be close to steady state, about 80% of the exchange has already occurred at 40 s. It is indeed surprising that already at 25 s, the time of the tissue-peak, about half of the exchange already occurred. So it appears unclear if extended scan times are really necessary and if the additional dose is not better used by more frequent sampling earlier. We suspect that the perceived necessity of longer scan times might be derived from DCE MRI, where the flow extraction product is determined from protocols lasting up to 5 min. But this is not due to physiology but to completely different relative signal change. While MRI has the potential to measure enough signal of the extravascular contrast medium portion the measurable changes in CT correspond to only a few HU. In the past, the relationship between quantifiable DCE-CT perfusion parameters and their potential clinical relevance remained relatively unclear. Therefore the majority of existing scientific studies aimed to include as many of the available perfusion parameters as possible. However, as mentioned previously, most of the literature appears to show that BV, BF and FE/PS in tumors are not independent; they mostly differ in a similar fashion from normal tissue or they change in the same direction as a result of therapy. It is certainly too early to draw this conclusion at the moment and it might also not be true for all types of tumors. Nevertheless, if

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Fig. 6. 56 year old patient with a primary neuroendocrine tumor of the small bowel and suspected metastasis after resection of the primary tumor. A 45 s dynamic study covering the whole liver (17.6 cm) was performed in shallow breathing on a 3rd generation dual-source CT system (SOMATOM Force, Siemens Healthcare Sector, Forchheim, Germany). The effective dose of the acquisition was 14 mSv. MIP projection of the arterial phase temporal maximum intensity projection (tMIP). (a) MIP projection of the portal phase tMIP. (b) MPR of the arterial phase average. (c) MPR of the portal phase average, hypovascular lesion (arrow). (d) MPR of the total tMIP. (e) TACs in normal liver (green) and metastasis (magenta). (f) Blood volume: normal liver 10, metastasis 3 ml/100 ml. (g) Portal venous flow : normal liver 75, metastasis 25 ml/100 ml/min. (h) Arterial hepatic flow.

a lower accuracy for permeability measures can be accepted at least for certain tumors, because it does not negatively affect the clinical use, overall examination time could be shortened resulting in lower dose and practical advantages. Ultimately, efforts to reduce the dose to the absolute minimum possible for a specific task will require customization depending on targeted perfusion parameters, the applied therapy as well as different tumor type. For longitudinal studies this might include more simplified models like the maximum slope model for flow or normalized peak enhancement as a blood volume surrogate marker with fewer data points and further reduction of examination time and radiation dose.

7. Contrast medium injection protocol It is generally recognized that a rapid, i.e., short, injection with a saline bolus chaser of the same flow rate is beneficial for all CT perfusion approaches. The majority of recent studies use injection times of not more than 10 s. This is driven by the focus on the firstpass for flow determination. In general, contrast injection protocols should aim at that the resulting AIF has a well-pronounced peak, is only slightly asymmetric when returning to baseline, and returns to baseline before recirculation (Fig. 5). The peak enhancement of the AIF can be considered an approximate scaling factor for the level of contrast enhancement in tissue during first pass. Therefore, for the same injection time, peak

enhancement and tissue contrast only depend on the iodine delivery rate (IDR). Thus, it is important to realize that increasing the IDR also helps reduce dose because images with higher noise levels will have the same CNR. If CM (concentration 300 mg/ml) injected at 4 ml/s (IDR = 1.2 g/s) is compared to CM (concentration 370 mg/ml) injected at 5 ml/s (IDR = 1.85 g/s), the contrast is 50% higher. This alone achieves the same CNR at half the dose as long as the system still performs well at the lower level. An injection time of not more than 10 s is also beneficial for the analysis of the liver parenchyma perfusion. The shortest mesenteric transit time is of the same magnitude and a short injection better separates the arterial and portal venous portions of the liver enhancement curves. And ultimately, short injections also support shorter examination times since the tissue response to the input function occurs earlier.

8. Future outlook: function and morphology from one examination DCE-CT imaging in oncology still remains a relatively new and rapidly evolving field. Until now, one has to acknowledge that the technique has not reached full maturity, which is mainly based on the lack of consensus regarding the indication and standardized robust acquisition protocols Fig. 6a–h The clearly required prospective multicenter (multivendor) studies still suffer from a typical

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dilemma. In order to include many sites, the study design often needs to be restricted to the largest common denominator among participants; this might not necessarily include the best technology currently available. Moreover, CT vendors need to support multivendor studies to help the technique to create outcome-oriented evidence about the usefulness. Otherwise, the implementation of CT perfusion into clinical practice as well as phase III clinical studies with CT perfusion parameters instead of morphologic measurements for response assessment will not become reality. However, with the increasing availability of CT scanners offering volumetric DCE-CT imaging and low noise detector technology this hopefully change within the near future. There is another more practical obstacle that has slowed down the gradual transfer of DCE-CT imaging into a more routine clinical setting outside of controlled imaging studies. It is necessary to perform it as a separate, extra examination with additional contrast and radiation dose which is also currently not reimbursed. Thus, it would definitely help quantitative DCE-CT perfusion imaging to enter into more clinical routine use if it could be effectively merged with standard staging or follow-up CT examinations performed in oncology. Dynamic low kV imaging with a detector optimized for low photon fluxes and a tube that provides similar image quality at low kV settings might enable this merging in the future. Standard high quality morphological phase images can easily be reconstructed from the 4D data by simply averaging adjacent time points after registration with the additional benefit that new image types like temporal maximum intensity projection or temporal average images can be generated. Fischer et al. recently reported that the conspicuity of hyper-vascular liver lesions is significantly higher if the maximum over the whole arterial phase is used [45]. If low kV settings at sufficiently high power are available, it would also be possible to add a standard larger range venous scan (abdomen, abdomen and thorax) immediately after the DCECT study without a new CM injection. Although the total amount of iodine is lower, this can be fully compensated for by the increased iodine contrast at low kV. In summary, the use of low kV X-ray settings in conjunction with optimized detection technology for low photon fluxes provides the largest leverage to reduce radiation dose to a reasonable level. Secondly, high temporal sampling, short scan times and optimized CM injection protocols are necessary to provide accurate quantitative DCE-CT perfusion results. Thirdly, substitution of standard contrast enhanced morphological scanning by volume DCE-CT scan merits further exploration in order to provide morphological high quality images and functional information within a single examination. References [1] V. Goh, Q.S. Ng, K. Miles, Computed tomography perfusion imaging for therapeutic assessment: has it come of age as a biomarker in oncology? Invest. Radiol. 47 (1) (2012) 2–4. [2] T.P. Yeung, G. Bauman, S. Yartsev, E. Fainardi, D. Macdonald, T.Y. Lee, Dynamic perfusion CT in brain tumors, Eur. J. Radiol. (2015). [3] W.H. Sommer, C.R. Becker, M. Haack, et al., Time-resolved CT: angiography for the detection and classification of endoleaks, Radiology 263 (3) (2012) 917–926. [4] W.A. Kalender, W. Seissler, E. Klotz, P. Vock, Spiral volumetric CT with single-breath-hold technique, continuous transport and continuous scanner rotation, Radiology 176 (1) (1990) 181–183. [5] K.A. Miles, M.P. Hayball, A.K. Dixon, Functional images of hepatic perfusion obtained with dynamic CT, Radiology 188 (2) (1993) 405–411. [6] D. Gandhi, E.G. Hoeffner, R.C. Carlos, I. Case, S.K. Mukherji, Computed tomography perfusion of squamous cell carcinoma of the upper aerodigestive tract: initial results, J. Comp. Assisted Tomogr. 27 (5) (2003) 687–693. [7] V. Goh, S. Halligan, J.A. Hugill, P. Bassett, C.I. Bartram, Quantitative assessment of colorectal cancer perfusion using MDCT: inter- and intraobserver agreement, AJR Am. J. Roentgenol. 185 (1) (2005) 225–231. [8] S. Bisdas, M. Baghi, J. Wagenblast, et al., Differentiation of benign and malignant parotid tumors using deconvolution-based perfusion CT imaging: feasibility of the method and initial results, Eur. J. Radiol. 64 (2) (2007) 258–265.

[9] R. Goetti, S. Leschka, L. Desbiolles, et al., Quantitative computed tomography liver perfusion imaging using dynamic spiral scanning with variable pitch: feasibility and initial results in patients with cancer metastases, Invest. Radiol. 45 (7) (2010) 419–426. [10] T. Kobayashi, T. Hayashi, S. Funabasama, S. Tsukagoshi, M. Minami, N. Moriyama, Three-dimensional perfusion imaging of hepatocellular carcinoma using 256-slice multidetector-row computed tomography, Radiat. Med. 26 (9) (2008) 557–561. [11] K.A. Miles, T.Y. Lee, V. Goh, et al., Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography, Eur. Radiol. 22 (7) (2012) 1430–1441. [12] S.M. Schuetze, L.H. Baker, R.S. Benjamin, R. Canetta, Selection of response criteria for clinical trials of sarcoma treatment, Oncologist 13 (Suppl 2) (2008) 32–40. [13] J. Shi, G. Schmid-Bindert, C. Fink, et al., Dynamic volume perfusion CT in patients with lung cancer: baseline perfusion characteristics of different histological subtypes, Eur. J. Radiol. 82 (12) (2013) e894–900. [14] D. Spira, P. Adam, C. Linder, et al., Perfusion and flow extraction product as potential discriminators in untreated follicular and diffuse large B cell lymphomas using volume perfusion CT with attempt at histopathologic explanation, AJR Am. J. Roentgenol. 198 (6) (2012) 1239–1246. [15] V. Goh, S. Halligan, S.A. Taylor, D. Burling, P. Bassett, C.I. Bartram, Differentiation between diverticulitis and colorectal cancer: quantitative CT perfusion measurements versus morphologic criteria–initial experience, Radiology 242 (2) (2007) 456–462. [16] M. Ronot, T. Asselah, V. Paradis, et al., Liver fibrosis in chronic hepatitis C virus infection: differentiating minimal from intermediate fibrosis with perfusion CT, Radiology 256 (1) (2010) 135–142. [17] M. Guo, Y. Yu, Application of 128 slice 4D CT whole liver perfusion imaging in hepatic tumor, Cell Biochem. Biophys. 70 (1) (2014) 173–178. [18] J. Singh, S. Sharma, N. Aggarwal, R.G. Sood, S. Sood, R. Sidhu, Role of perfusion CT differentiating hemangiomas from malignant hepatic lesions, J. Clin. Imag. Sci. 4 (2014) 10. [19] K. Hayano, G.S. Desai, A.R. Kambadakone, J.M. Fuentes, K.K. Tanabe, D.V. Sahani, Quantitative characterization of hepatocellular carcinoma and metastatic liver tumor by CT perfusion, Cancer Imag. 13 (4) (2013) 512–519. [20] G. Grozinger, R. Plentz, M. Horger, Education and imaging: hepatobiliary and pancreatic: volume-perfusion CT for monitoring therapy for hepatocellular carcinoma, J. Gastroenterol. Hepatol. 28 (3) (2013) 588. [21] J.C. Yao, A.T. Phan, K. Hess, et al., Perfusion computed tomography as functional biomarker in randomized run-in study of bevacizumab and everolimus in well-differentiated neuroendocrine tumors, Pancreas 44 (2) (2015) 190–197. [22] G. Chen, D.Q. Ma, W. He, B.F. Zhang, L.Q. Zhao, Computed tomography perfusion in evaluating the therapeutic effect of transarterial chemoembolization for hepatocellular carcinoma, World J. Gastroenterol. 14 (37) (2008) 5738–5743. [23] D. Ippolito, D. Fior, P.A. Bonaffini, et al., Quantitative evaluation of CT-perfusion map as indicator of tumor response to transarterial chemoembolization and radiofrequency ablation in HCC patients, Eur. J. Radiol. 83 (9) (2014) 1665–1671. [24] E. Squillaci, G. Manenti, C. Ciccio, et al., Perfusion-CT monitoring of cryo-ablated renal cells tumors, J. Exp. Clin. Cancer Res. 28 (2009) 138. [25] S. Bisdas, S.A. Nguyen, S.K. Anand, G. Glavina, T. Day, Z. Rumboldt, Outcome prediction after surgery and chemoradiation of squamous cell carcinoma in the oral cavity, oropharynx, and hypopharynx: use of baseline perfusion CT microcirculatory parameters vs tumor volume, Int. J. Radiation Oncol. Biol. Phys. 73 (5) (2009) 1313–1318. [26] G. d’Assignies, A. Couvelard, S. Bahrami, et al., Pancreatic endocrine tumors: tumor blood flow assessed with perfusion CT reflects angiogenesis and correlates with prognostic factors, Radiology 250 (2) (2009) 407–416. [27] K. Hayano, S. Okazumi, K. Shuto, et al., Perfusion CT can predict the response to chemoradiation therapy and survival in esophageal squamous cell carcinoma: initial clinical results, Oncol. Rep. 18 (4) (2007) 901–908. [28] M. Bellomi, G. Petralia, A. Sonzogni, M.G. Zampino, A. Rocca, CT perfusion for the monitoring of neoadjuvant chemotherapy and radiation therapy in rectal carcinoma: initial experience, Radiology 244 (2) (2007) 486–493. [29] J. Wang, N. Wu, M.D. Cham, Y. Song, Tumor response in patients with advanced non-small cell lung cancer: perfusion CT evaluation of chemotherapy and radiation therapy, AJR Am. J. Roentgenol. 193 (4) (2009) 1090–1096. [30] S. Bisdas, Z. Rumboldt, J. Wagenblast, et al., Response and progression-free survival in oropharynx squamous cell carcinoma assessed by pretreatment perfusion CT: comparison with tumor volume measurements, AJNR Am. J. Neuroradiol. 30 (4) (2009) 793–799. [31] M.S. Park, E. Klotz, M.J. Kim, et al., Perfusion CT noninvasive surrogate marker for stratification of pancreatic cancer response to concurrent chemo- and radiation therapy, Radiology 250 (1) (2009) 110–117. [32] S.P. Li, A. Makris, A. Gogbashian, I.C. Simcock, J.J. Stirling, V. Goh, Predicting response to neoadjuvant chemotherapy in primary breast cancer using volumetric helical perfusion computed tomography: a preliminary study, Eur. Radiol. 22 (9) (2012) 1871–1880. [33] L. Preda, S.F. Calloni, M.E. Moscatelli, M. Cossu Rocca, M. Bellomi, Role of CT perfusion in monitoring and prediction of response to therapy of head and neck squamous cell carcinoma, BioMed. Res. Int. 2014 (2014) 917150.

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[34] S. Sudarski, J. Shi, G. Schmid-Bindert, et al., Dynamic volume perfusion computed tomography parameters versus RECIST for the prediction of outcome in lung cancer patients treated with conventional chemotherapy, J. Thoracic Oncol. 10 (1) (2015) 164–171. [35] F. Morsbach, B.R. Sah, L. Spring, et al., Perfusion CT best predicts outcome after radioembolization of liver metastases: a comparison of radionuclide and CT imaging techniques, Eur. Radiol. 24 (7) (2014) 1455–1465. [36] B.F. Tomandl, E. Klotz, R. Handschu, et al., Comprehensive imaging of ischemic stroke with multisection CT, Radiographics 23 (3) (2003) 565–592. [37] M. Wintermark, P. Maeder, F.R. Verdun, et al., Using 80 kVp versus 120 kVp in perfusion CT measurement of regional cerebral blood flow, AJNR Am. J. Neuroradiol. 21 (10) (2000) 1881–1884. [38] C. Chefd’hotel, G. Hermosillo, O. Faugeras, Flows of diffeomorphisms for multimodal image registration, Proc. 1st IEEE Int. Symp. Biomed. Imag. (ISBI\ 02) (2002), http://dx.doi.org/10.1109/ISBI.2002.1029367, pp. 753–756. [39] C.S. Reiner, R. Goetti, D. Eberli, et al., CT perfusion of renal cell carcinoma: impact of volume coverage on quantitative analysis, Invest. Radiol. 47 (1) (2012) 33–40. [40] C. Saddi KCh, F. Cheriet, Large deformation registration of contrast-enhanced images with volume-preserving constraint, in: Proc SPIE 6512, Medical Imaging 2007, 2007.

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[41] R. Goetti, C.S. Reiner, A. Knuth, et al., Quantitative perfusion analysis of malignant liver tumors: dynamic computed tomography and contrast-enhanced ultrasound, Invest. Radiol. 47 (1) (2012) 18–24. [42] J.A. Christner, K. Stierstorfer, A.N. Primak, C.D. Eusemann, T.G. Flohr, C.H. McCollough, Evaluation of z-axis resolution and image noise for nonconstant velocity spiral CT data reconstructed using a weighted 3D filtered backprojection (WFBP) reconstruction algorithm, Med. Phys. 37 (2) (2010) 897–906. [43] U. Haberland, E. Klotz, N. Abolmaali, Performance assessment of dynamic spiral scan modes with variable pitch for quantitative perfusion computed tomography, Invest. Radiol. 45 (7) (2010) 378–386. [44] J.G. Korporaal, C.A. van den Berg, C.R. Jeukens, et al., Dynamic contrast-enhanced CT for prostate cancer: relationship between image noise, voxel size, and repeatability, Radiology 256 (3) (2010) 976–984. [45] M.A. Fischer, B. Leidner, N. Kartalis, et al., Time-resolved computed tomography of the liver: retrospective, multi-phase image reconstruction derived from volumetric perfusion imaging, Eur. Radiol. 24 (1) (2014) 151–161.

Please cite this article in press as: E. Klotz, et al., Technical prerequisites and imaging protocols for CT perfusion imaging in oncology, Eur J Radiol (2015), http://dx.doi.org/10.1016/j.ejrad.2015.06.010