Image-Guided Thermal Therapy Using Magnetic Particle Imaging and Magnetic Fluid Hyperthermia

C H A P T E R

10 Image-Guided Thermal Therapy Using Magnetic Particle Imaging and Magnetic Fluid Hyperthermia Rohan Dhavalikar*, Ana C. Boho´rquez†, Carlos Rinaldi*,‡ *Department of Chemical Engineering, University of Florida, Gainesville, FL, United States Herbert Wertheim College of Engineering’s Research Service Centers (RSC), University of Florida, Gainesville, FL, United States ‡ J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States

†

10.1 INTRODUCTION Magnetic nanoparticles (MNPs) have been used for diagnostic imaging [1] and in cancer thermal therapy by hyperthermia, alone [2] and in combination with chemotherapeutics [3], due to their ability to generate nanoscale heat. The therapeutic use of heat in cancer is one of the earliest therapy approaches described in the history of medicine. In recent decades, it has been demonstrated that cancer cells resistant to ionizing radiation or chemotherapy are likely to be more sensitive to hyperthermia [4]. Mitochondrial dysfunction in cancer causes insufficient cellular respiration, thus modifying the way that normal cells produce energy [5]. Cancer cells derive most of their energy from lactic acid buildup, which generates an acidic pH in the cell microenvironment [6]. This acidic environment

Nanomaterials for Magnetic and Optical Hyperthermia Applications https://doi.org/10.1016/B978-0-12-813928-8.00010-7

increases the probability that cell death will arise upon delivery of a specific hyperthermic dose. One way to measure the hyperthermic dose is estimating cumulative equivalent minutes (CEMs), where time and temperature recorded during hyperthermia therapy as treatment variables are standardized to an equivalent number of minutes at 43°C (CEM43°C) [7]. In the clinic, three heating approaches are used to deliver heat in many types of cancer ensuring hyperthermic doses needed to affect cancer cells. Depending on the extent of the cancer lesion being treated, heat delivery approaches are classified as local hyperthermia, regional hyperthermia, and whole-body hyperthermia [8]. In whole-body hyperthermia, external heating has been achieved through thermal incubator chambers, hot water blankets, and electric blankets. In regional hyperthermia, several

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approaches have been used to heat large areas of tissue, such as regional perfusion or continuous hyperthermic peritoneal perfusion [9]. For local hyperthermia, radiofrequency ablation, and microwave ablation therapies have been employed [10,11]. They incorporate external applicators or antennas emitting microwaves or radiofrequencies, making these procedures highly invasive for anything other than superficial tumors and limited to small cancer lesions. Alternatively, high-intensity focused ultrasound (HIFU) uses heat generated by focusing ultrasound waves in a small tumor area [12]. Laser interstitial thermal therapy is an emerging type of local hyperthermia where a laser catheter limits thermal energy only to a small cancer lesion [13]. Even though there is a wide variety of heating approaches under consideration, challenges such as a lack of temperature homogeneity during treatment, patient discomfort due to hot spots and burns, and limitations in gaining access and treating deep-seated tumors have restricted the clinical use of thermal approaches in cancer therapy. The use of nanotechnology has enormous potential to overcome heat delivery and specificity challenges in hyperthermia. The ability to manipulate cancer cells intracellularly with nanoparticles could fuel new breakthroughs in healthcare such as improving medical imaging, redefining local hyperthermia technologies, and allowing the development of more efficient drug delivery systems. To improve the heating efficiency in local hyperthermia, multimodal imaged-guided photothermal therapy using particles that absorb energy in the near-infrared (NIR) region has been achieved with gold nanoparticles [14], quantum dots [15], and carbon nanotubes [16]. Also, HIFU ablation, applied in conjunction with nanoparticles, has increased the rate of HIFU ablation by reducing the acoustic energy required to cause heating and thermal effects in tissue [17,18]. Finally, magnetic fluid hyperthermia (MFH), defined as the local heating of MNPs induced by alternating magnetic fields (AMFs), has been widely investigated

in vitro and in vivo [2,19–25]. A major advantage of MFH in comparison with other thermal approaches mentioned earlier is that magnetic fields are unaffected by tissue depth, hence, magnetic fields can penetrate deep inside the biological system to trigger the heat response of MNPs. MNPs designed for MFH applications already include physical, chemical, and biological features, and adequate magnetic heating properties. However, noninvasive and real-time temperature measurement of the treated region, as well as off target accumulation and subsequent heating of MNPs remain open challenges. Magnetic particle imaging (MPI) [26] is an emerging tomographic imaging technology which makes use of the nonlinear magnetization response of MNPs to generate an image. As MPI provides high spatial and temporal resolution, and provides excellent contrast due to the signal being generated solely by MNPs, it has been explored for various biomedical applications from vascular stenosis imaging [27,28] to cancer imaging [29], with other applications under development. The combination of the field gradient used in MPI with MFH shows potential to overcome the nonspecific heating problem [30–32] and provide noninvasive realtime feedback through MPI images regarding the thermal dose. In this chapter, the first section describes in detail the concept of MFH, heating mechanisms, equipment setup, and nanomaterials that have been utilized for MFH. It also reports challenges associated with the implementation of MFH. The second part introduces MPI, the physics behind image generation, equipment design, and signal reconstruction methods. The introduction to MPI is followed by a discussion of the nanomaterials used in MPI and some of the recent applications of MPI. Later sections discuss the potential of combining MPI and MFH to address the issue of nonspecific heating in MFH and introduce the concept of image-guided thermal therapy using MPI. Finally, we outline the ideal particle properties

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for MPI-MFH applications and comment on future directions for this promising imagedguided thermal therapy.

10.2 MAGNETIC FLUID HYPERTHERMIA MFH is a noninvasive and externally controlled cancer treatment that utilizes biocompatible MNPs for heat generation in the presence of an externally applied AMF. Exposure of tumor cells to increased temperature has been shown to sensitize cells for chemotherapy and induce cell death [33]. The following sections discuss the mechanisms of heat generation, the metrics for such heat generation [such as the specific absorption rate (SAR)], describe the construction of equipment for treatment, and the biological effects of MFH. These sections also report the nanomaterials used for MFH and highlight some of the challenges that need to be addressed for successful clinical translation.

10.2.1 Heat Dissipation Mechanisms and SAR Heat generation by MNPs subjected to AMFs can be related to their relaxation losses and depends on the area of the dynamic magnetization hysteresis loop. The dominant magnetic relaxation mechanism for energy dissipation corresponds to the mechanism with the shortest relaxation time τ. The commonly accepted relaxation processes for MNP suspensions are rotational Brownian motion, where particles with fixed dipoles rotate physically, and N
eel relaxation, where the dipoles rotate internally [34]. When the reorientation of the magnetic dipole moment of a particle suspended in a liquid is tied to the rotation of the particle itself, the Brownian relaxation time is given by the following equation: πηD3h 3Vh η ¼ τB ¼ 2kB T kB T

(10.1)

where η is the viscosity of the suspended fluid, Dh is the hydrodynamic particle diameter, Vh is the hydrodynamic volume, T is the temperature, and kB is the Boltzmann constant. The N
eel theory of superparamagnetism explains that a spontaneous change of the magnetization direction commonly occurs in singledomain particles under the influence of thermal fluctuations, thus leading to the expression [35]

 1 KV KV ¼ τ0 exp (10.2) τN ¼ exp f0 kB T kB T where K is the anisotropy constant, V is the volume of the particle, f0 is the attempt frequency factor, and the inverse of attempt frequency is the attempt time τ0 in the order of 109 s. The relative contributions of the Brownian and N
eel relaxation times are related to an effective relaxation time τ according to 1 1 1 ¼ + τ τB τN

(10.3)

Thus, the relaxation time τ of the magnetization of the suspension corresponds to the shorter of the times τN and τB. A way to determine the heating performance of MNPs under an AMF is through the SAR. Theoretically, the rate of heat dissipation per mass of monodisperse non-interacting MNPs is given by P (10.4) SAR ¼ ρMNPs ϕ where P is the volumetric power dissipation, ρMNPs is the density of the MNPs, and ϕ is the volume fraction of the nanoparticles in solution. An expression for the volumetric power dissipation has been obtained by Rosensweig [36] and is given as P¼

μ0 π 2 Md d3 ϕfH2 2πf τ ¼ μ0 πχ 00 ð f ÞfH 18kB T 1 + ð2πf τÞ2 (10.5)

where μ0 is the permeability of free space, Md is the magnetic material domain magnetization, d

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is the magnetic core diameter, kB is Boltzmann’s constant, T is the absolute temperature, τ is the magnetic relaxation time of the MNPs, χ 00 is the out-of-phase component of the susceptibility, f is the frequency, and H is the peak amplitude of the applied AMF. SAR values strongly depend on the physical (size and polydispersity) and magnetic properties of the nanoparticles, such as magnetic diameter, saturation magnetization, magnetocrystalline anisotropy constant, and mechanism of magnetic relaxation. Experimentally, to calculate the SAR of MNPs in suspension while exposed to an AMF, the following equation is used X  Cpi mi ΔT   ¼ Cp  ΔT (10.6)  SAR ¼ mFe Δt t¼0 mFe Δt where the initial rate of change in temperature for the suspended MNPs upon application of an AMF is the ratio between ΔT (temperature) and Δt (time), Cpi is the heat capacity of fluid components of mass mi in the MNP suspension and mFe is the iron mass in suspension per unit mass of fluid. SAR values can vary with field frequency (f ) and field strength (H) of the applied AMF. To compare experimental SAR values obtained for different values of f and H, one can use the intrinsic loss power (ILP) [37] ILP ¼

SAR H2 f

(10.7)

In addition to understanding the heating rate of MNPs, it is important to quantify the thermal dose delivered to cancer cells treated with MFH. As mentioned earlier, CEM43°C is a widely used parameter in hyperthermia to assess the efficacy of heating under different thermal conditions, if there is a measurable increase in temperature during hyperthermia treatment. This term introduced by Sapareto and Dewey [7] proposed the use of the Arrhenius relationship to normalize thermal data from hyperthermia treatment (thermal isoeffective dose). The equation for CEM43°C is

CEM43°C ¼

n X

ti  Rð43Ti Þ

(10.8)

i¼1

where CEM43°C is the cumulative number of equivalent minutes at 43°C (temperature most commonly used for normalization purposes, also called as the break temperature), ti is the time interval (min), T is the average temperature during the time interval ti, R is an experimental value related to the number of minutes needed to compensate for one degree of temperature change either below or above the break temperature depending upon the cell line type. Hence, the R-value could be interpreted as the temperature dependence of the rate of cell death. R-value is 0.5 when T is above 43°C and R is 0.25 when T is below 43°C in Chinese hamster ovary cells [38]. However, there is a lack of noninvasive and real-time temperature measurement methods to measure the increase in temperature in a treated region during MFH, which makes it difficult to use CEM43°C as a thermal dose parameter. Furthermore, it has been demonstrated that targeted intracellular hyperthermia can kill cancer cells without a perceptible rise in temperature [20,21]. In this scenario, nanoscale heating is responsible for cell damage and the CEM43°C becomes irrelevant, as the macroscopic temperature does not change. In this case, it has been suggested that the total heat dose (THD) be used as the relevant parameter. The THD for a given treatment time t is given by [20] THD ¼ mcell  SAR  t

(10.9)

where mcell is the number of nanoparticles internalized per cell, usually estimated as mass of iron in the case of iron oxide MNPs.

10.2.2 Typical Instrumentation for MFH In a laboratory setting, an MFH setup consists of a combination of a high-frequency magnetic field source equipped with a water-cooled solenoid coil designed for in vitro or in vivo

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applications. Commonly, an induction heating device is used, where an alternating current supply generates a high-frequency magnetic field inside the solenoid coil (resonant resistanceinductance-capacitance, RLC, circuit). The solenoid coil determines the field strength amplitude at a specific current resonance. The resonance frequency can be theoretically calculated using the equation 1 (10.10) f ¼ pffiffiffiffiffiffi 2π LC where L is the inductance of the solenoid coil and C is its capacitance. The magnetic field generated at an axial position z measured from the midplane of a finite solenoid coil of radius a, with N turns, and current I is given by [39] 0 1 L L B C z + z+ C K0 B 2 2 C " # " # + H¼ B

 B C 2 2 2@ 1 1A L L + a2 2 + a2 2 z z+ 2 2 (10.11) where K0 ¼ NI L . As can be seen in Eq. (10.11), the magnetic field intensity is highest in the center of the solenoid coil (z ¼ 0), and it is reduced at the ends of the coil. Shorter coils have poor axial field uniformity, which can cause significant nonuniformity of the magnetic flux density in MFH applications, leading to inconsistent heat deposition in tissue. For this reason, a coil design generating a homogeneous magnetic field in a large tissue lesion is critical to produce uniform heat delivery in MFH. Additional accessories that can be used in a MFH system include an incubator chamber to maintain a constant control sample temperature, humidity, and environmental CO2 mimicking cell incubator conditions for in vitro experimentation. For in vivo experimentation, anesthesia delivery systems may be required when treating with the AMF. Moreover, to monitor temperature simultaneously during application of

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AMF, fiber optic temperature probes with a digital recording temperature system or thermal imaging infrared cameras can be used during the hyperthermia treatment. For clinical application, MagForce Nanotechnologies AG (Berlin, Germany) has developed a human-sized magnetic field applicator, the NanoActivator (previously called MFH 300F), which generates a 100 kHz AMF at a variable field strength of 0–18 kA m1. This field generator consists of a ferrite yoke with pole shoes resting above and below the patient bed aperture and covering only part of the patient bed. This magnetic yoke is coupled with a resonant circuit of coils and capacitors to generate the magnetic field and also control it. The aperture distance is adjustable, and a sliding bed allows for movement of the patient into the aperture [40,41]. Refer to Chapter 5 for a more detailed discussion on MFH instrumentation.

10.2.3 Biological Effects of MFH Hyperthermia causes numerous important and complex changes in tissue and cell physiology in cancer. The cytotoxic effects of heat in cancer cells have been well-documented, and have been found to be temperature and time dependent [42,43]. Mainly, heat deposition in cancer cells at hyperthermic temperatures (41–45°C) produces lipid transitions, protein denaturation, and affects lipid fluidity, all of which have been modeled as Arrhenius rate processes [44]. Furthermore, hyperthermia induces vasodilatation, increasing blood supply [45], and increased cell uptake of drugs [46–48], making it an effective adjuvant for chemotherapy and radiotherapy [33]. Research is still ongoing to study effects on intracellular compartments involved in hyperthermic killing, such as damage to the nuclear, lysosomal, mitochondrial, and endoplasmic reticular components of the cell. However, studies that are concentrated in magnetically modulated energy delivery using MNPs and

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its effects in cell biology are severely limited. In a MFH study, varying degrees of membrane blebbing from the plasma membrane were observed, accompanied with disruption of the actin and tubulin cytoskeletons. This disruption of the cytoskeleton was thought to have caused cell death [49]. Cell death by apoptosis is described by several events such as membrane blebbing, DNA fragmentation, and cell contraction [50], thus suggesting that apoptosis is the mechanism of cell death in the study by Prasad and collaborators. Other biological findings such as rapid production of heat shock proteins (HSPs) [51], membrane fluidization [46], and lysosomal membrane permeabilization causing activation of lysosomal death pathways [21,52] have been identified as biological responses of cancer cells treated with MFH and nanoscale heating by MNPs. An immediate strategy to understand the biology inside the cell in hyperthermia mediated by MNPs would be to incorporate the wide variety of cell death assays used in drug discovery to understand cell death mechanisms [53]. Other approaches, such as immunofluorescence techniques could be incorporated as a regular test to visualize intracellular changes before, during, and after MFH therapy. Also, nanoscale thermometry and measurement of the nanoviscosity inside cells may help to monitor changes in viscosity because of protein denaturation and

aggregation with the production of HSPs, due to local thermal effects induced by MNPs. Furthermore, understanding the mechanisms of cell death without a macroscopic temperature rise could provide insights for effective nanoscale thermal delivery, making nanoscale heating with MNPs a highly promising alternative for cancer treatment.

10.2.4 Magnetic Nanomaterials for MFH MNPs with distinct properties have been synthesized and explored for MFH applications. Iron oxide nanoparticles, being widely regarded as biocompatible, have been extensively studied. The size, shape, magnetic properties, and coating material have been shown to influence the performance of these nanoparticles in hyperthermia and other biomedical applications. Table 10.1 summarizes some of the iron oxide MNPs used in MFH. For biomedical applications, substituted ferrites using Mn [64], Zn [65], and Co [66] have also been synthesized and explored as potential candidates for MFH. However, core-shell structures [67] or metallic-doping species, such as Zn, Co, Mn, and Au have not been evaluated extensively in terms of toxicity and biodegradability in vitro and in vivo. Also, safety limit considerations in the selection of frequency and magnetic field amplitude conditions for the applied AMF are often not taken into

TABLE 10.1

Examples of Iron Oxide Nanoparticles With Different Configurations Explored for MFH

Shape

Physical Size (nm)

Frequency (kHz)

Field strength (kA m21)

SAR (W g21Fe)

ILP (nHm2 kg21)

References

Spherical

10–20

343

10–37

80–200

2–6

[54,55]

Cubic

12–47

320–700

10–64

100–2400

Max. 5.7

[56–60]

a

Max. 3.1

[61,62]

Max. 5.9

[63]

Multicore Nanoflowers a

13–17 4–24

900–950 300–800

1

6–12 21

128–613

a

140–3420

1

Value converted from W g Fe3O4 to W g Fe.

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consideration when estimating SAR values. Because of the lack of understanding about the effect of exposure to AMF on tissues, the product of the frequency and magnetic field amplitude of 4.85  108 Am1 s1 has been considered as a safe operation guideline in MFH. Thus, high energy dissipation values of MNPs obtained at frequency and magnetic field amplitude conditions above the safety considerations should be studied in more detail.

10.2.5 Engineering Challenges in MFH MNPs can be designed with targeting functionalities, allowing selective cellular uptake and intracellular thermal damage [49,68,69]; while molecular and cellular imaging capabilities support examination of the particles within tissues, organs, and cells [70]. Nanoparticle physical-chemical-biological features can impact delivery efficiency of MNPs to reach solid tumors, perhaps, allowing the design of novel drug delivery systems for potentiation of chemotherapeutic agent effects [71]. Also, MNPs can be designed to enhance MFH therapy by facilitating high energy deposition [55] or controlling magnetic relaxation mechanism contributions under AMF [72]. Beyond MFH applications, MNPs can be designed for magnetic capture in vivo [73], or for enhanced magnetic imaging capabilities using magnetic resonance imaging (MRI) or MPI. Nanoparticle engineering plays a pivotal role in the enhancement of stability and mobility of MNPs in crowded biological environments [74], which determine circulation time of MNPs in the blood stream, as particle sizes below 100 nm have shown accumulation through the enhanced permeation and retention (EPR) effect in solid tumors [75,76]. Delivery of MNPs to solid tumors for MFH is not a trivial task, but is a critical factor to advance clinical applications of MFH. So far, delivery efficiencies of nanoparticles remain low in solid tumors (1% of total injected dose)

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after intravenous injection [77]. Although, tumor-associated macrophage sequestration plays a pivotal role in the enhancement of MNP uptake in solid tumors [78], most administered MNPs can be either sequestered by the liver and spleen or affected by kidney clearance [79,80]. This considerably reduces the thermal dose that can be delivered to tumors, limiting the use of whole-body hyperthermia applicators after systemic delivery of MNPs. Thus, to address selective uptake by liver and spleen and to extend the circulation lifetime of MNPs in vivo, polyethylene glycol (PEG)coated MNPs have been developed. However, PEG-coated MNPs formulations have not yet fully resolved the sequestration problem by the liver and spleen [81], and limited particle uptake in solid tumors via passive and active targeting has been reported [82]. Also, preclinical studies using whole body hyperthermia have shown damage to liver, a nontarget organ [83]. Thus, there is a need to design systems to avoid nonspecific heating by nanoparticles that accumulate in off-target organs. Another challenge in MFH is temperature monitoring in the tumor. Primarily, fiber-optic probes are used for temperature measurements during MFH [23,84], but this approach is limited to registering the temperature at the location of the probe and does not record the temperature of the entire region under treatment. Also, the procedure to place the probes is highly invasive and tends to be difficult in the case of deeply seated tumors. Thus, noninvasive temperature monitoring techniques are essential to obtain temperature distribution in the tumor to administer a therapeutic heat dose. One solution to overcome some of these challenges is through the combination of the field gradient used in MPI with MFH. This approach is described later in the chapter and shows potential to reduce off-target heating and provide a noninvasive temperature monitoring method.

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10.3 MAGNETIC PARTICLE IMAGING Introduced in 2005 [26], MPI is an emerging tracer imaging technology with high sensitivity, zero depth attenuation, high contrast, fast image acquisition and reconstruction, and submillimeter resolution. It relies on the nonlinear magnetization response of MNPs to an AMF superimposed with a scanned field gradient to obtain the spatial distribution of nanoparticles. MPI utilizes biocompatible iron-oxide nanoparticles as tracers and provides excellent contrast due to the absence of host-tissue background signal. It also serves as a safer alternative to radioactive tracer technologies like positron emission tomography and single-photon emission computed tomography. MPI has been employed in a number of biomedical applications for cardiovascular imaging [85], stem cell tracking [86], traumatic brain injury (TBI) imaging [87], and cancer imaging [29]. The following sections explain the physics of MPI, equipment design, signal reconstruction methods, nanoparticles used in MPI, and introduces some recent applications of MPI.

10.3.1 MPI Physics MPI relies on the ability of MNPs to respond to external magnetic fields to generate images of their distribution. At equilibrium, magnetization response of MNPs to the applied magnetic field is often described by the Langevin function [34]. The Langevin function shows a linear magnetization response at small fields, a nonlinear magnetization response at moderate field strengths, and a nearly constant magnetization response at high field strengths. MPI makes use of this characteristic behavior to generate a signal, which is further processed to produce an image of particle concentration distribution in a field of view (FOV). The particles primarily respond to a change in magnetic field by the N
eel relaxation mechanism, Brownian

relaxation mechanism, or a combination of both. In the case of N
eel relaxing particles, the magnetic dipole moments rotate internally to align with the direction of the field, whereas in the case of Brownian relaxing particles the magnetic dipole moment is fixed with the particle easy axis and the particle rotates physically to align with the direction of the field. This change in dipole direction results in a change in the magnetization of the particle. According to Faraday’s law of induction, the change in magnetization induces a voltage in a closed-circuit loop surrounding the magnetic particles. This voltage is also referred to as the MPI signal. To introduce spatial selectivity and generate an image of particle concentration, a magnetic field gradient is applied to create a field-free region (FFR) and a saturated region, as illustrated in Fig. 10.1. Dipoles in the FFR are in a state of randomness whereas dipoles in the saturated region are aligned in the direction of the strong applied field. On superposition of an AMF on the static field gradient, the dipole moments in the FFR change direction, resulting in the change in magnetization and thus induction of a voltage. In contrast, the rotation of the dipole moments in the saturated region is restricted due to the strong static field, resulting in a tiny change in the magnetization and thus a small signal. The position of the FFR can be moved in a sequence to scan the region of interest. By accurately determining the position of the FFR at a given time and matching the induced voltage response with the FFR position, an image of the particle concentration distribution can be obtained.

10.3.2 Equipment Setup MPI scanners consist of a set of solenoid coils producing a strong static magnetic field gradient in three directions and separated by a distance in Maxwell configuration. The field produced by these coils is called the selection field [26] or the bias field [88]. The solenoid coils

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FIG. 10.1

Illustration of MPI physics showing the response of magnetic nanoparticles in a field-free region and a saturated region created by a selection field. For particles in the saturated region, the presence of a strong selection field restricts the rotation of the magnetic dipole moment in an alternating drive field. This leads to minor oscillations in the magnetization response and thus a negligible signal. Whereas, for particles in the field-free region, the absence of a magnetic field permits free rotation of the magnetic dipole in response to an alternating drive field. The sinusoidal magnetization response results in generation of a large signal. By accurately gridding the signal with the position of the field-free region, the signal can be transformed into an MPI image.

can also be replaced with strong NdFeB permanent magnets. Maxwell configuration of the coils leads to formation of a FFR, in either a field-free line (FFL) or field-free point, depending on the number of coils. This FFR can be translated mechanically or electronically to scan the entire FOV. Another set of coils, called the drive field or excitation coils, are placed along a different axis to help move the FFR electronically. The alternating current in the drive coils produces an AMF that causes changes in magnetization of particles in the FFR, resulting in a signal. The location of the FFR can be controlled either mechanically or electronically and is also influenced by the drive field. The path followed by the FFR is known as a pulse sequence, and Lissajous, Cartesian, radial, and spiral trajectories [89,90] have been explored so far. The primary goal of the pulse sequence

is to scan the region of interest in the most optimal manner. The innermost coil surrounding the region of scanning is called the receive coil. This coil serves to detect the nanoparticle signal generated due to the motion of the FFR. The magnetic particles located in the FFR of the selection field are free to respond to the AMF produced by the drive coils. Due to the change in magnetization of the particles, a voltage (signal) is induced in the receive coils surrounding the FOV [91]. The particles located at a distance from the FFR are in a state of magnetic saturation and do not induce a voltage (signal) in the receive coil as their magnetization does not change much with the application of an AMF. The induced voltage from tracers in the FFR contains information regarding the tracer concentration and is further processed to generate images.

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10.3.3 Signal Reconstruction The two main methods for processing the acquired signal to obtain the spatial distribution of the concentration of the nanoparticles are (i) system matrix based or harmonic space reconstruction [92–94] and (ii) X-space reconstruction [88,95,96]. An illustration explaining the two reconstruction methods is shown in Fig. 10.2. In the system matrix-based reconstruction, due to the nonlinear response of the particles, the Fourier transform of the signal shows higher harmonics of the fundamental drive field frequency. The higher harmonics contain information unique to the tracer. To obtain a concentration profile of the particles, it requires a system calibration obtained by moving a point sample filled with the tracer in the FOV. The system matrix contains information regarding the behavior of the particles at every point in space. The spatial distribution of the concentration is obtained by inversion of this system matrix. This process can be time consuming as the scanner is scaled up for human applications. Also, the system matrix is acquired for tracers in water and hence may not represent their actual behavior in the in vivo environment. In X-space reconstruction, a direct reconstruction of particle concentration is possible using the signal. The signal

is converted to an MPI image in two steps, velocity compensation and signal gridding to the instantaneous position of the FFR. This approach is less time consuming as it does not involve completing a calibration scan and does not involve the complexity of the matrix inversion process.

10.3.4 Nanomaterials Used for MPI In parallel with hardware improvement, several efforts are underway for improving the performance of tracers used in MPI. Initial theoretical predictions indicated that the resolution in MPI could be improved by using particles with large core diameters [97]. The theoretical resolution increases cubically with diameter and linearly with gradient strength. The first paper on MPI in 2005 [26] made use of a commercially available MRI contrast agent, Resovist. Resovist consists of clusters of 4 nm-sized iron-oxide nanoparticles coated with carboxymethyl dextran and was the “gold standard” for comparison. Resovist is no longer available commercially, however ferucarbotran, another nanoparticle formulation with comparable properties as Resovist is commercially available and used for comparison. The magnetic material for the tracers has been iron oxide due to its biocompatibility, while

FIG. 10.2 The system function approach makes use of a system matrix obtained from a superparamagnetic iron oxide (SPIO) sample with a known concentration, which is measured at every point in the scanning region. The signal spectrum obtained by finite Fourier transformation of the signal in the field-free region (FFR) shows decaying harmonic spectra whereas in the saturated region the signal spectrum is almost negligible. In the X-space approach, a calibration scan of the SPIO sample is not required and the MPI image is obtained by gridding the time series signal with the precise location of the FFR.

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10.4 APPLICATIONS

efforts have been made to improve surface coatings in order to provide finer MPI resolution, high signal-to-noise ratio (SNR), and sensitivity. Hence, recent investigations on synthesis, modification, and characterization of the engineered MNPs have allowed to achieve safe and efficient tracer distribution in vivo and superior tracer response for MPI applications [55]. The thermal decomposition method, based on thermolysis of iron (III) oleate with excess oleic acid in organic solvents, has been used extensively for particle synthesis [98]. Thus, phase transfer after particle synthesis from organic to aqueous phase is required and is often achieved using biocompatible polymers, for example, PEG. On assessing the performance of these particles in MPI scanners, these particles performed better as compared to Resovist, which was attributed to more uniform size and optimal magnetic properties. The use of PEG coatings supports nanoparticle biocompatibility and specific functionality. Also, changing the molecular weight of the PEG has been shown to have a profound effect on the colloidal stability of the particles and their blood half-life [99]. PEG-coated particles are primarily suited for vascular applications due to enhanced blood circulation time, which is in part because PEG reduces the opsonization process in vivo [100]. However, for some applications of MPI, such as in cancer imaging, the tracers need to be functionalized with specific targeting ligands to reach the target site. In this context, lactoferrin-conjugated particles showed specific internalization in C6 glioma cells without loss of MPI signal and thus could potentially be used for brain glioma imaging [101]. Also, different particle synthesis approaches could significantly affect MPI particle performance, which was observed by Heinke et al. [102] in their attempt to mimic the biomineralization process observed in magnetotactic bacteria by using a modification of the basic coprecipitation reaction. In their work, the particle synthesis took place in a hydrogel network offering

control over crystal growth by modifying the diffusion rates of reactants and minimizing convection. In comparison with commercially available magnetic particles, FeraSpin R, they found better performance of their particles. As compared to alkaline coprecipitation performed in solution, where the crystal size is between 2 and 17 nm, this synthesis approach could generate particles with a mean size of 24 nm. In an attempt to improve phase purity of the synthesized nanoparticles from W€ ustite phase to the desired magnetite phase, postsynthesis oxidation was carried out by another research group [103]. In a more recent synthesis approach, molecular oxygen was introduced during the thermal decomposition synthesis in a controlled manner to synthesize monodispersed iron oxide nanoparticles with similar magnetic and physical diameters. These particles were single-crystalline magnetite iron oxide nanoparticles with diminished dead layer and had improved SNR and resolution as compared to those synthesized in the absence of oxygen [55]. Tracer development is an important aspect of MPI and the future success of MPI will largely depend on the improvement of tracer synthesis and optimization for MPI applications.

10.4 APPLICATIONS MPI is primarily designed to image one particle type at a time, but the possibility of separating signals obtained from different particle types or particles in different environments and assigning them different colors for visualization of particle systems in a single image was recently explored [104]. A multicolor reconstruction approach was used to differentiate particle types and aggregation states. The multicolor reconstruction approach uses the difference in the amplitude and phase information unique to each particle system or binding states of the same particle system to separate the signal. This initial work indicated that the separation of two particle

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systems measured simultaneously is feasible without modification of current system hardware or measurement sequence. This feature could be vital in discriminating signal originating from bound versus freely floating tracers and was recently used to discern a guide wire coated with a Resovist varnish from a lumen of a vessel phantom filled with diluted Resovist [105]. Although traumatic brain injury (TBI) is quite prevalent in the United States, identifying and classifying its severity is still a difficult task. MPI’s excellent contrast makes it suitable for blood pool imaging, as the signal is produced only by the nanoparticles remaining in the blood. This feature of MPI was used to acquire images of internal bleeding caused by TBI [87]. This work demonstrated for the first time the ability of MPI to image TBI in order to determine the location and the severity of the injury. The work showed the potential of implementing MPI as a fast and noninvasive imaging modality in an emergency clinic setting for internal bleeding diagnosis. Clinicians depend heavily on imaging modalities to track cancer progression. MPI, with its high sensitivity, ideal contrast, and ability to image biocompatible iron-oxide tracers anywhere in the body, can serve as an imaging platform to track tumor progression. In vivo detection of breast tumor in athymic nude rats after systemic delivery of particles was demonstrated for the first time using MPI [29]. MPI was able to visualize initial rim enhancement of the tumor due to the EPR effect, followed by accumulation of particles in the tumor, and then clearance from the tumor into liver and spleen. The quantitative nature of MPI was demonstrated and utilized in determining tracer biodistribution and tracer dynamics after systemic delivery of the particles.

10.5 COMBINED MPI-MFH MPI is an emerging imaging modality with numerous applications still under exploration. The field gradient generated in MPI for

acquiring nanoparticle concentration distribution can also be employed for localizing MFH. One of the challenges in MFH is to prevent nonspecific heating damage to organs, which accumulate particles intended for tumor accumulation on application of an AMF. MPI field gradients provide a simple solution to focus heat dissipation. The following sections explain the effect of static field on heating rate by MNPs, describe the heating region tuning ability of MPI field gradients, show evidence of selective heating achievable by MPI field gradients, introduce image-guided thermal therapy using MPI, and outline ideal characteristics of tracers suitable for MPI-MFH.

10.5.1 Influence of Static Field on Heat Dissipation by MNPs The presence of a strong DC field leads to alignment of the dipoles in the direction of the field. This DC field also restricts changes in dipole orientation, thus inhibiting their rotation in the presence of an AMF. As explained in the heating mechanism section, the applied magnetic field does work on the dipoles to change the dipole orientation, which is dissipated as heat. In the presence of an AMF and absence of a static field, the dipoles are free to respond to the AMF and create a dynamic hysteresis loop, which is broad as shown in Fig. 10.3. The area inside the loop being large, the particles dissipate a large amount of heat. However, if the particles are exposed to a strong static field, the particles are in a state of saturation and hence do not respond easily to the AMF. This restricted motion leads to a narrow dynamic hysteresis loop, which has smaller area. As the area in the loop corresponds to the amount of energy dissipated by the particles, a smaller amount of heat is generated in the presence of a static field. The reduced heat dissipation is attributed to the change in the magnetization response of the dipoles to an AMF in the presence of a strong DC field. In an experiment [106] where a static field was applied along with an alternating field to Resovist, temperature

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FIG. 10.4 Theoretical predictions of specific absorption rate (SAR) curve showing a bell-shaped distribution. Due to the presence of a field gradient, the energy dissipated by particles in the high bias field is reduced while maximum energy is dissipated in the absence of a bias field [30].

FIG. 10.3 Dynamic hysteresis loop obtained by solving the Martensyuk, Raikher, and Shliomis (MRSh) equation for frequencies used in magnetic fluid hyperthermia under the influence of a bias or static magnetic field. The increase in the field strength of the bias field shows a decrease in the area of the hysteresis loop [30].

recorded by optic fiber temperature probe showed a small temperature rise in a strong DC magnetic field as compared to the temperature recorded when static field was absent.

10.5.2 Tuning Region of Heating Using a Field Gradient The influence of static field on the heating rate can be further extended to be utilized with a spatially varying DC field. A field gradient can be

utilized to focus heat dissipation from MNPs [107–110]. MPI produces a field gradient to facilitate imaging of MNP concentration. This field gradient can be translated to localize the FFR over the region of treatment. In the presence of a field gradient, the SAR distribution shows a bell-shaped curve where the highest energy dissipation is observed at zero bias and a reduced SAR at high bias field, as illustrated in Fig. 10.4. The sharpness of the SAR distribution can be altered by changing the field gradient strength, as shown in Fig. 10.5. The increase in field gradient strength could be achieved by increasing the field generated by electromagnetic coils or by utilizing high-grade permanent magnets. The field gradient can also be changed by varying the distance between the coils or the magnets producing the static field. Field gradients of up to 5.58 kA m2 can be achieved using current MPI scanners [86,111] and can be utilized to achieve millimeter-scale theoretical heating resolution, as shown in Fig. 10.5. Experimental studies [110] have shown focusing of heating region by varying the current in the solenoid coils producing the static field separated by

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FIG. 10.5 The applied field gradient strength has a profound effect on the region of heating. With field gradients of 5.58 kA m2, the heating region can be theoretically tuned to millimeter distances and shows a potential to reduce nonspecific heating damage away from the FFR.

a fixed distance. In another set of studies, narrowing in the shape of the SAR distribution curve was observed with increase in field gradient [106]. In that study, SAR values were obtained from an empirical equation and from the experimental rate of temperature rise.

10.5.3 Selective Heating Spatial selection of the heating region could be achieved by putting into practice a magnetic field gradient. A system has been designed to generate a FFR using solenoid coils excited with DC current flowing in opposite direction [110]. An AC solenoid was added in between the DC solenoid to generate an alternating field. In the in vitro experiments, three plastic cups filled with ferrofluid were placed inside the AC solenoid, separated by a distance of 2 cm. Upon exposure to AC fields, temperature was recorded in each cup using fiber optic temperature probes. The central cup in the FFR showed the highest temperature rise as compared to those away from the FFR. The FFR was translated by applying different DC current magnitudes to the DC solenoid

coils leading to reduced heating in the central cup. These observations were utilized to check in vivo feasibility, where ferrofluid was injected percutaneously in adult rat tails and the tails were placed in the setup. Histological studies of the tail showed maximum burn damage in the FFR where the static field is minimum, thus showing potential of selective heating. Another hyperthermia setup designed to test the heating efficiency of zinc-doped iron oxide nanoparticle samples was modified to generate a region with small magnetic field strength using repelling permanent magnets, which were placed on the exterior of the AC magnetic field producing coil [112]. Thermal camera images of a three-dimensional (3D) printed multiwell sample holder were recorded and showed reduced image intensity in the wells located in the high field strength region as compared to the well located in the region of small magnetic field strength. More recently, an MPI setup was modified to utilize its magnetic field gradient to selectively heat MNP vials separated by a distance of 3 mm [31]. In this study, selective and successive heating of the vials was achieved by translating and aligning the FFR with each of the vials in the phantom. A sequential temperature rise was observed using dextran coated iron oxide nanoparticle vials that were aligned with the FFL. Negligible temperature rise was observed in the control vial filled with phosphate buffered saline (PBS) solution, placed adjacent to the nanoparticle filled vials. The observed spatiotemporal selective heating is shown in Fig. 10.6. These studies show the potential of on-demand selective heating of a desired region with millimeter scale precision and minimal nonspecific heating of healthy tissue surrounding the tumor while by-passing organs like liver and spleen which readily take up MNPs. This potential was realized in recent in vivo studies where selective heating was observed in a U87MG mouse xenograft tumor model while sparing the liver [32].

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FIG. 10.7 Simultaneous MPI-MFH shows qualitative FIG. 10.6 Sequential heating of spatially separated vials is achieved by aligning the FFL with each vial while alternating magnetic field is being applied. On-demand heating can be realized through MPI field gradient. From D. W. Hensley, Z. W. Tay, R. Dhavalikar, B. Zheng, P. Goodwill, C. Rinaldi, S. Conolly, Combining magnetic particle imaging and magnetic fluid hyperthermia in a theranostic platform. Phys. Med. Biol. 62 (2017) 3483–3500. doi: https://doi.org/10. 1088/1361-6560/aa5601. # Institute of Physics and Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved.

10.5.4 Image-Guided Thermal Therapy Using MPI Another challenge in MFH is to noninvasively determine the amount of heat deposited by the MNPs at a particular location. One way to address this limitation is by conducting in vitro heating studies to determine temperature rise and heat deposition rate for a specific field application time. Simulations could also be executed to predict temperature rise [113–115] and SAR values to help in the treatment planning process [30, 116]. However, both these approaches are limited in their applications due to the complexity of the in vivo environment and ability to mimic it in simulations

agreement between MPI signal PSF and SAR curve. This opens up the field of MFH for real-time feedback using MPI to determine heat deposition in a selected area. From D. W. Hensley, Z. W. Tay, R. Dhavalikar, B. Zheng, P. Goodwill, C. Rinaldi, S. Conolly, Combining magnetic particle imaging and magnetic fluid hyperthermia in a theranostic platform. Phys. Med. Biol. 62 (2017) 3483–3500. doi: https://doi. org/10.1088/1361-6560/aa5601. # Institute of Physics and Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved.

and in vitro studies. One potential solution to overcome these limitations is using real-time feedback systems which relate an image acquired from the treatment region with the amount of heat deposited in that region. In studies of MPI-MFH, qualitative agreement was observed between a MPI point spread function (PSF) that was acquired while applying a heating sequence and the SAR curve [31]. This comparison is shown in Fig. 10.7 and suggests the ability of MPI signal to provide real-time SAR quantification. More recent in vivo studies show good correlation between MPI image intensity and the deposited SAR, thus providing spatial predictions of SAR dose [32]. Also, the harmonics that constitute the MPI signal could be used to noninvasively determine the temperature of the treatment area [117–120].

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10.5.5 Designing Nanoparticles for MPI-MFH The potential application of MPI for imageguided thermal therapy opens up a whole new field of particle optimization studies. This emerging theranostic platform will benefit the most from MNPs that provide excellent contrast and resolution, while dissipating large amount of heat. The SAR of MNPs is often related to the relaxation mechanism, namely Brownian and N
eel. The size of the particles determines the relaxation mechanism and high SAR values can be obtained by increasing the particle size. But, SPIOs larger than 20 nm relax primarily by the Brownian mechanism and can introduce blurring in MPI, affecting resolution and signal strength [121,122]. Thus, the large size MFH particles relaxing by the Brownian mechanism may not be the best candidates for MPI, requiring further optimization studies. An ideal MPI-MFH nanoparticle would primarily relax by the N
eel mechanism to facilitate imaging as well as heat release due to internal dipole moment rotation. This would be crucial in applications where the particle’s physical movement is restricted or hampered due to the complex in vivo environment. The resolution of the image is affected by the core diameter of the particles [123], thus continued efforts to minimize magnetically dead layer in large size synthesized nanoparticles relaxing by the N
eel mechanism [55] and controlling size [54] will remain critical in advancing tracer development. Similarly, SAR values have been shown to increase with an increase in the particle size [54,55,124,125], and thus provide a guideline in optimizing particle size for heat delivery. A narrow size distribution of particles would be beneficial in both MFH and MPI to obtain uniform magnetic response to applied fields and to better understand their interaction with the surroundings. The state of particle suspension has been shown to affect both MFH and MPI performance. In MFH applications, an

increase in the hydrodynamic size of the aggregates was found to decrease the SAR values [126], most probably due to blocking of the Brownian component of the particles. Similarly, MPI simulations for Brownian particles have shown a drop in signal with an increase in hydrodynamic size [121,122]. However, in another study it was found that 20 nm aggregates of Resovist formed from 5 nm N
eel particles perform at par with single-core 20 nm particles [127]. Thus, for MPI-MFH applications, an aggregate structure consisting of larger size N
eel particles could increase MPI signal without significantly affecting the SAR values. Also, to achieve better targeting and accumulation in tumors, the MNP surface would need to be modified with receptor targeting ligands. Finally, to achieve successful clinical translation, the particles need to be colloidally stable in vivo and have long blood circulation time.

10.6 CONCLUSION This chapter provides a brief overview of MFH, a promising cancer treatment method that uses biocompatible MNPs for depositing energy when subjected to an AMF, in combination with MPI as an approach for image-guided spatially selective nanoscale thermal therapy. The chapter outlined the heat dissipation mechanisms and the nanomaterials used for achieving hyperthermia temperatures. The chapter also highlighted the challenges of MFH for successful clinical translation. MPI, an emerging tomographic tracer imaging technology that makes use of a field gradient for spatial localization and image generation was introduced. MPI physics, scanner construction, and signal reconstruction methods were discussed briefly in the chapter, followed by nanomaterials for MPI and some recent MPI applications. To address the problem of nonspecific heating and heating damage to healthy tissue during MFH, the usefulness of field gradients was

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Acknowledgment This work was supported by the University of Florida and National Institutes of Health (1R21EB018453-01A1).

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