Hayden Carlton

Spatially confined magnetic nanoparticle thermal therapy (SC-MNTT) can improve the tumor coverage index (CI) when the magnetic field-free region (FFR) is manually steered. However, static steering demands significant time and expertise from experienced control engineers during treatment planning. This study presents a computational framework for automated, dynamic FFR steering combined with real-time tumor boundary temperature control to enhance magnetic nanoparticle thermal therapy (MNTT) for cancer therapy. The framework also supports controlled MNTT treatment planning. We integrated the Pennes bioheat transfer equation with a proportional-integral-derivative (PID) controller to maintain the maximum tumor boundary temperature at 46 °C. The magnetic nanoparticle (MNP) heat source was divided into four subdomains to simulate SC-MNTT. A fuzzy logic controller automatically selected the FFR based on four-point temperature measurements and corresponding thermal dose, aiming to maximize CI. We examined the influence of key treatment parameters: maximum treatment temperature (60 or 80 °C), MNP distribution (uniform or Gaussian), sensor noise, and MNP position relative to the tumor center. Automated dynamic FFR steering increased CI by ∼25-35%, depending on these parameters. The highest CI occurred at a maximum treatment temperature of 80 °C with a uniform MNP distribution. Sensor noise had minimal effect, while minimal offset between the MNP position and tumor center was essential for maximizing CI. The proposed fuzzy logic-based dynamic FFR steering may reduce reliance on expert control engineers for SC-MNTT treatment planning. Future work will focus on pre-clinical implementation and experimental validation of the control system.

Targeting tumors with nanoparticles can significantly improve the anti-tumor effect of thermal treatments while sparing normal tissue from nonspecific effects. The surface coating is essential to driving the biological fate of nanomaterials. Exosomes, a type of long-circulating secreted vesicle, are capable of specific cell targeting and present a variety of molecules on the surface. Differences among physiochemical properties and protein expression of the exosome isolates were quantified based on the origin of the exosomes. Extrusion through polycarbonate membranes was used to coat nanoparticles with these membranes. TEM was used to visualize cell uptake in vitro, and biodistribution was quantified using ICP-MS following intravenous injection in mice bearing 4T1 breast tumors. Alternating magnetic field was used to heat cells after incubation with nanoparticles in vitro. In vivo The exosome coating promoted particle immunoevasion and accumulation in different organs, though it limited extravasation within the tumor as well, independent of the source cell line. TEM of individual tumor cells showed that particles with exosome coating are taken up more rapidly than uncoated particles and these particles accumulate in multivesicular bodies. Particles were incubated in vitro with 4T1 tumor cells and heat treated. The exosome-coated particles combined with magnetic heating led to a greater decrease in tumor cell viability than with uncoated particles. Significant changes in biodistribution, cellular uptake, and heating induced by alternating magnetic field were observed with these formulations. Studies are ongoing to identify the proteins needed to maximize tumor accumulation and efficacy of thermal therapy.

We have previously verified the capabilities of a prototype magnetic nanoparticle (MNP) heater (called HYPER) that can perform spatially confined heating; however, the design lacked temperature control capabilities. In this work, we designed, verified, and validated a relay-based autotuning proportional–integral–derivative (PID) controller to be used with the HYPER during in vivo experiments. The PID controller is an autotuning, relay-based controller with several design constraints: The controller must: (1) maintain tumor temperature within hyperthermic range of 41–46 °C; (2) rise time ≤ 5 min; (3) steady-state temperature must be within ±0.5 °C of the setpoint; (4) standard deviation of steady-state temperature within ±0.5 °C; and (5) temperature overshoot within 5%. The relay-based autotuning PID controller was designed in LabVIEW® with real-time thermal dose monitoring. Verification experiments were performed by heating aqueous suspensions of high-performance iron oxide MNPs. For validation, we injected the MNPs into tumor-bearing mice and analyzed the ability of the controller to maintain in vivo temperature. The results of the study show that controller was able to maintain the temperature within the hyperthermic range with a rise time ∼4 min and steady-state error ∼0.1 °C. Validation was performed on six mice, where four mice showed the temperature was maintained within design criteria and two mice partially met the design criteria. The autotuning controller can maintain the temperature within the design criteria and monitor thermal dose in real-time.

Clinical magnetic nanoparticle hyperthermia therapy (MNHT) requires controlled energy deposition to achieve a prescribed tumor thermal dose. The objective of this work is to design a thermal dose feedback control to deliver prescribed Cumulative Equivalent Minutes at 43 [°C] (CEM43) based on values at selected tumor boundary points. Constraints were imposed to maintain the maximum treatment temperature below 60 [°C] and the tumor boundary at ∼ 43 [°C]. The controller was designed by performing an integrated system dynamic - finite element analysis. Finite element-bioheat transfer (FE-BHT) simulations were performed on a computational phantom developed from the imaging data of a de-identified human head divided into voxels representing the skull, cerebrospinal fluid (CSF), brain, tumor, and ventricles. A uniform distribution of magnetic nanoparticles (MNPs) in an ellipsoid was used to represent MNPs in the phantom tumor. The MNP distribution was subdivided into three domains to simulate the steerable spatially confined heating region during MNHT. Proportional-integral-derivative (PID) control and model predictive control (MPC) were explored. Regions of the phantom tumor that were undertreated during the simulated MNHT were selectively heated by adjusting the heating volume to improve the tumor coverage index (CI; tumor volume ≥ CEM43 of 20 [min]). Results show that steerable spatially confined heating improves CI by ∼15%. MPC achieves CI of 80% faster than PID (67 [min] vs. 80 [min]). Simulations demonstrated the feasibility of automated control to deliver tumor conformal thermal doses using steerable spatially confined heating.

Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8–10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag^®-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics^®. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning.

Magnetic particle hyperthermia is an approved cancer treatment that harnesses thermal energy generated by magnetic nanoparticles when they are exposed to an alternating magnetic field (AMF). Thermal stress is either directly cytotoxic or increases the susceptibility of cancer cells to standard therapies, such as radiation. As with other thermal therapies, the challenge with nanoparticle hyperthermia is controlling energy delivery. Here, we describe the design and implementation of a prototype pre-clinical device, called HYPER, that achieves spatially confined nanoparticle heating within a user-selected volume and location. Spatial control of nanoparticle heating was achieved by placing an AMF generating coil (340 kHz, 0-15 mT), between two opposing permanent magnets. The relative positions between the magnets determined the magnetic field gradient (0.7 T/m-2.3 T/m), which in turn governed the volume of the field free region (FFR) between them (0.8-35 cm³). Both the gradient value and position of the FFR within the AMF ([-14, 14]_x, [-18, 18]_y, [-30, 30]_z) mm are values selected by the user via the graphical user interface (GUI). The software then controls linear actuators that move the static magnets to adjust the position of the FFR in 3D space based on user input. Within the FFR, the nanoparticles generate hysteresis heating; however, outside the FFR where the static field is non-negligible, the nanoparticles are unable to generate hysteresis loss power. We verified the performance of the HYPER to design specifications by independently heating two nanoparticle-rich areas of a phantom placed within the volume occupied by the AMF heating coil.

Purpose: Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. Methods: Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne’s bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. Results: Simulations predicted increased thermal damage (10–15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. Conclusion: A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning.

Heating magnetic nanoparticles (MNPs) with alternating magnetic fields (AMFs) have applications in biomedical research and cancer therapy. Accurate measurement of the heating efficiency or specific loss power (SLP) generated by the MNPs is essential to assess response(s) in biological systems. Efforts to develop standardized equipment and to harmonize results obtained from various MNP samples and AMF systems have met with little success. Without a standardized magnetic nanoparticle or calorimeter device, objective comparisons of estimated thermal output among laboratories remain a challenge. In addition, the most widely used adiabatic initial slope model fails to account for thermal losses, which are unavoidable. We propose a non-adiabatic method to analyze MNP heating efficiency derived from the Box–Lucas equation, wherein the sample is subjected to several short duration heating pulses. SLP is then estimated from an arithmetic average of the Box–Lucas fitted coefficients obtained from each pulse. Heating experiments were conducted with two identical samples that were placed within vessels having different thermal insulation using the same AMF parameters. Though the samples generated different temperature curves, the pulsed Box–Lucas method produced nearly equivalent SLP estimates. Further, the pulsed test enabled analysis of the heat transfer coefficient providing quantitative measures of sample heat loss throughout the test, with robust statistical confidence. We anticipate this new methodology will aid efforts to standardize measurements of MNP heating efficiency, enabling direct comparison among varied systems.

The process of magnetic nanoparticle heating releases enormous amounts of thermal energy. Through typical calorimetric analyses, the total thermal energy released can be easily quantified; however, knowledge of nanoscale temperature is necessary. Herein, a novel method of nanoscale thermometry by analyzing intra‐particle diffusion in core–shell nanoparticles is proposed. Heating the iron cores with an alternating magnetic field in a saline suspension encourages the diffusion of sodium ions into the silica shells of the particles, which is modeled numerically; however, experimental measurements are needed in order to provide accurate diffusivity estimations. After determining the diffusion characteristics from X‐ray photoelectron spectroscopy) depth profiling of silica films, energy dispersive analysis with high‐resolution transmission electron microscopy measures the sodium ion gradient within single particles before and after heating. When compared directly to the numerical simulations, the results indicate that the temperature gradient between particles and saline suspension reaches significantly higher temperatures than the macro‐scale temperature of the solution. By accurately knowing the thermal gradient between nanoparticles and the surrounding medium, nanoparticles can be engineered to limit surface resistances as much as possible and promote high rates of thermal energy transfer.

This paper presents an efficient physics-based electro-thermal model that solves some advanced problems of modeling Silicon Carbide (SiC) power MOSFETs. It is the first electro-thermal model that simulates the temperature dependency of the first and the third quadrant characteristics, including the reverse recovery of the body diode accurately and efficiently. It extends from a previous work that demonstrated the isothermal physics-based model of the gate-dependent body diode. Physics-based temperature scaling of the first and third quadrant allows simulation of the self-heating effect in a wide range of temperatures (27°C – 200°C), even for the synchronous operation. Moreover, a physics-based modeling approach is taken to include gate-voltage dependent non-linearity of the gate to source capacitance (Cgs). Also, a physic-based segmented cascaded method is taken to accurately model the Miller (Crss), and the output (Coss) capacitances at the low and very high drain to source voltage regions. Further, the temperature-dependent breakdown mechanism is included for reliable system design. Double Pulse Tests (DPTs) at various temperatures up to 200°C validate the model's accuracy. Lastly, a synchronous buck converter test demonstrates the model's ability to predict junction temperatures, validating the model's accuracy and efficiency in a continuous operation with self-heating.