V Stakhursky

Real-time MRI-guided hyperthermia treatment using a fast adaptive algorithm

Magnetic resonance (MR) imaging is promising for monitoring and guiding hyperthermia treatments. The goal of this work is to investigate the stability of an algorithm for online MR thermal image guided steering and focusing of heat into the target volume. The control platform comprised a four-antenna mini-annular phased array (MAPA) applicator operating at 140 MHz (used for extremity sarcoma heating) and a GE Signa Excite 1.5 T MR system, both of which were driven by a control workstation. MR proton resonance frequency shift images acquired during heating were used to iteratively update a model of the heated object, starting with an initial finite element computed model estimate. At each iterative step, the current model was used to compute a focusing vector, which was then used to drive the next iteration, until convergence. Perturbation of the driving vector was used to prevent the process from stalling away from the desired focus. Experimental validation of the performance of the automatic treatment platform was conducted with two cylindrical phantom studies, one homogeneous and one muscle equivalent with tumor tissue (conductivity 50% higher) inserted, with initial focal spots being intentionally rotated 90 degrees and 50 degrees away from the desired focus, mimicking initial setup errors in applicator rotation. The integrated MR-HT treatment platform steered the focus of heating into the desired target volume in two quite different phantom tissue loads which model expected patient treatment configurations. For the homogeneous phantom test where the target was intentionally offset by 90 degrees rotation of the applicator, convergence to the proper phase focus in the target occurred after 16 iterations of the algorithm. For the more realistic test with a muscle equivalent phantom with tumor inserted with 50 degrees applicator displacement, only two iterations were necessary to steer the focus into the tumor target. Convergence improved the heating efficacy (the ratio of integral temperature in the tumor to integral temperature in normal tissue) by up to six-fold, compared to the first iteration. The integrated MR-HT treatment algorithm successfully steered the focus of heating into the desired target volume for both the simple homogeneous and the more challenging muscle equivalent phantom with tumor insert models of human extremity sarcomas after 16 and 2 iterations, correspondingly. The adaptive method for MR thermal image guided focal steering shows promise when tested in phantom experiments on a four-antenna phased array applicator.

Online feedback focusing algorithm for hyperthermia cancer treatment

Purpose: Magnetic resonance (MR) imaging is increasingly being utilized to visualize the 3D temperature distribution in patients during treatment with hyperthermia or thermal ablation therapy. The goal of this work is to lay the foundation for improving the localization of heat in tumors with an online focusing algorithm that uses MR images as feedback to iteratively steer and focus heat into the target. Methods: The algorithm iteratively updates the model that quantifies the relationship between the source (antenna) settings and resulting tissue temperature distribution. At each step in the iterative process, optimal settings of power and relative phase of each antenna are computed to maximize averaged tumor temperature in the model. The MR-measured thermal distribution is then used to update/correct the model. This iterative procedure is repeated until convergence, i.e. until the model prediction and MR thermal image are in agreement. A human thigh tumor model heated in a 140 MHz four-antenna cylindrical mini-annular phased array is used for numerical validation of the proposed algorithm. Numerically simulated temperatures are used during the iterative process as surrogates for MR thermal images. Gaussian white noise with a standard deviation of 0.3°C and zero mean is added to simulate MRI measurement uncertainty. The algorithm is validated for cases where the source settings for the first iteration are based on erroneous models: (1) tissue property variability, (2) patient position mismatch, (3) a simple idealized patient model built from CT-based actual geometry, and (4) antenna excitation uncertainty due to load dependent impedance mismatch and antenna cross-coupling. Choices of starting heating vector are also validated. Results: The algorithm successfully steers and focuses a tumor when there is no antenna excitation uncertainty. Temperature is raised to ≥43°C for more than about 90% of tumor volume, accompanied by less than about 20% of normal tissue volume being raised to a temperature ≥41°C. However, when there is antenna excitation uncertainty, about 40% to 80% of normal tissue volume is raised to a temperature ≥41°C. No significant tumor heating improvement is observed in all simulations after about 25 iteration steps. Conclusions: A feedback control algorithm is presented and shown to be successful in iteratively improving the focus of tissue heating within a four-antenna cylindrical phased array hyperthermia applicator. This algorithm appears to be robust in the presence of errors in assumed tissue properties, including realistic deviations of tissue properties and patient position in applicator. Only moderate robustness was achieved in the presence of misaligned applicator/tumor positioning and antenna excitation errors resulting from load mismatch or antenna cross coupling.

Improved hyperthermia treatment control using SAR/temperature simulation and PRFS magnetic resonance thermal imaging.

Purpose: This article explores the feasibility of using coupled electromagnetic and thermodynamic simulations to improve planning and control of hyperthermia treatments for cancer. The study investigates the usefulness of preplanning to improve heat localisation in tumour targets in treatments monitored with PRFS-based magnetic resonance thermal imaging (MRTI). Methods: Heating capabilities of a cylindrical radiofrequency (RF) mini-annular phased array (MAPA) applicator were investigated with electromagnetic and thermal simulations of SAR in homogeneous phantom models and two human leg sarcomas. High frequency structure simulator (HFSS) (Ansoft) was used for electromagnetic simulations and SAR patterns were coupled into EPhysics (Ansoft) for thermal modelling with temperature-dependent variable perfusion. Simulations were accelerated by integrating tumour-specific anatomy into a pre-gridded whole body tissue model. To validate this treatment planning approach, simulations were compared with MR thermal images in both homogenous phantoms and heterogeneous tumours. Results: SAR simulations demonstrated excellent agreement with temperature rise distributions obtained with MR thermal imaging in homogeneous phantoms and clinical treatments of large soft-tissue sarcomas. The results demonstrate feasibility of preplanning appropriate relative phases of antennas for localising heat in tumour. Conclusions: Advances in the accuracy of computer simulation and non-invasive thermometry via MR thermal imaging have provided powerful new tools for optimisation of clinical hyperthermia treatments. Simulations agree well with MR thermal images in both homogeneous tissue models and patients with lower leg tumours. This work demonstrates that better quality hyperthermia treatments should be possible when simplified hybrid model simulations are performed routinely as part of the clinical pretreatment plan.

Clinical utility of magnetic resonance thermal imaging (MRTI) for realtime guidance of deep hyperthermia

A critical need has emerged for volumetric thermometry to visualize 3D temperature distributions in real time during deep hyperthermia treatments used as an adjuvant to radiation or chemotherapy for cancer. For the current effort, magnetic resonance thermal imaging (MRTI) is used to measure 2D temperature rise distributions in four cross sections of large extremity soft tissue sarcomas during hyperthermia treatments. Novel hardware and software techniques are described which improve the signal to noise ratio of MR images, minimize motion artifact from circulating coupling fluids, and provide accurate high resolution volumetric thermal dosimetry. For the first 10 extremity sarcoma patients, the mean difference between MRTI region of interest and adjacent interstitial point measurements during the period of steady state temperature was 0.85°C. With 1min temporal resolution of measurements in four image planes, this noninvasive MRTI approach has demonstrated its utility for accurate monitoring and realtime steering of heat into tumors at depth in the body.

Clinical Utility of Magnetic Resonance Thermal Imaging (MRTI) For Realtime Guidance of Deep Hyperthermia

A critical need has emerged for volumetric thermometry to visualize 3D temperature distributions in real time during deep hyperthermia treatments used as an adjuvant to radiation or chemotherapy for cancer. For the current effort, magnetic resonance thermal imaging (MRTI) is used to measure 2D temperature rise distributions in four cross sections of large extremity soft tissue sarcomas during hyperthermia treatments. Novel hardware and software techniques are described which improve the signal to noise ratio of MR images, minimize motion artifact from circulating coupling fluids, and provide accurate high resolution volumetric thermal dosimetry. For the first 10 extremity sarcoma patients, the mean difference between MRTI region of interest and adjacent interstitial point measurements during the period of steady state temperature was 0.85°C. With 1min temporal resolution of measurements in four image planes, this noninvasive MRTI approach has demonstrated its utility for accurate monitoring and realtime steering of heat into tumors at depth in the body.

SU‐GG‐T‐367: Fast Hyperthermia Temperature Optimization for Pelvic Carcinoma Patient Treated in Sigma‐Eye Applicator

Purpose: Though hyperthermia shows promising features being used with radiation and chemotherapy, it requires accurate spatial power focusing, which leads a workload proportional to square of number of antennas in an applicator. This motivates this investigation of model reduction method for pelvic‐carcinoma patient treated in Sigma‐Eye applicator. Method and Materials: A patient placed in the middle ring of this 100 MHz 3‐ring 12‐antenna applicator was used to validate our approach. A ‘similar’ patient with different thermal property values, perfusion values and was placed between the middle and low ring was used to determined virtual source (VS) basis vectors. A VS vector is a weighted combination of magnitudes and phases of 12 antennas and was determined to maximize averaged tumor temperature. Physical variables were projected to a reduced VS subspace spanned by a few VS vectors. Temperature response functions of tumor and normal tissues were determined in this reduced subspace and then used in temperature optimization iteration process. Results: By comparing the optimized temperature elevation distributions, we found it is indeed feasible to use a few chosen (best) VS basis vectors to optimally treat a pelvic carcinoma patient in Sigma‐Eye applicator; even when we determined those virtual source basis vectors from an existing “similar” patient. Conclusion: This success suggests a faster and easier pre‐treatment temperature optimization approach that relives workloads of physicians.

SU‐GG‐J‐164: Real‐Time Magnetic Resonance Imaging (MRI) Guidance and Thermal Modeling to Focus Hyperthermia Delivery

Purpose: Hyperthermia is an effective adjuvant modality for treatment of locally advanced cancer. However, focusing heat in the tumor using an external microwave applicator can be difficult, due to electromagnetic wave reflections at tissue interfaces. We present a methodology to steer heating towards the desired focus in real‐time, using MR thermal images for feedback. Method and Materials: The treatment control platform is based on repeated MR proton resonance frequency shift thermal imaging of the treatment volume over the course of the treatment. A cylindrical applicator with 4 independent pairs of dipole patch antennas (140MHz) is used as a heat source. The control process consists of iteratively constructing and updating a model for the heated object. At each iteration, the current model is employed to compute the optimal antenna settings (settings that the model predicts to focus heating in the target). These settings are applied to obtain the next thermal image, which is utilized in the next iteration to update the model. Thus, the algorithm progressively steers focusing while updating the model. We report on the convergence efficiency of the algorithm and importance of prior system knowledge (pretreatment simulations). Results: The experiments conducted on a cylindrical muscle‐equivalent phantom demonstrated that, for the 4‐antenna applicator, 16 iterations were sufficient to converge to the optimal thermal coverage of tumor. If prior knowledge was used, only 12 iterations were necessary to reach convergence from a starting focus that is positionally rotated 90° with respect to the desired focus. For a smaller positional rotation of 50°, 3 iterations were sufficient for convergence. The ratio of tumor to normal tissue heating after convergence improved by a factor of 3–6, compared to the initial ratio. Conclusion: Real‐time adaptive thermal modeling enables fast convergence to the optimal treatment of the patient and corrects for dynamic system changes.

SU-FF-J-81: Estimation of Dose Variations During Prostate Radiation Treatment Due to Elastic Deformations of Soft Tissues

Objective: The true dose distribution is subject to change due to organ shifts and also deformations. The goal of this work is to estimate the effect of the elastic organ deformations on the dose distribution for the average prostate IMRTtreatment.Methods: A prostate patient was surveyed during the radiation treatment (IRB approved). The original treatment plan was created based on the planning CT. On‐board cone beam computed tomography(CBCT)images were taken before treatments for the first 5 days and once a week therafter. The prostate, seminal vesicles (SV), bladder and rectum segmented on each CBCT were deformably registered against the planning CT. The analysis was conducted with the help of C++ ITK image registration and segmentation package. Initially, a volume (e.g., prostate and bladder) segmented on the planning CT scan was aligned against corresponding volume on the daily CBCT scan using a translational filter. Then, the aligned volume on CBCT was elastically deformed (Finite Element elastic deformation method), and the computed deformation vector field was used to propagate the true delivered radiation dose distribution to the original CT‐based volume of the patient. Results: For the plan with a planning target volume (PTV) defined as a combination of both prostate and SV with a 5‐mm margin, the DVH parameters fluctuated as follows (Vx denotes percent volume above x% of the prescription dose): prostate mean V100 76%, mean V95 97%, compared to the original prostate 98.15% and 100%, rectum and bladder mean V50 49% and 75% correspondingly, compared to the original rectum and bladder 50% and 29%. Conclusions: This study shows that some deviations from the planned dose may occur due to elastic distortion of soft tissue geometry. The developed suite of applications can be used to monitor actual delivered dose to the targets and critical structures.