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.

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.

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.

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.

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK^® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

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.