Design of a High Selectivity Filter for MRI Guided RF Hyperthermia Therapy

Hyperthermia devices have been integrated with MR scanners to exploit MR thermometry. Integrating two RF systems require the filtering of high-power RF heating signal from MR system for simultaneous heating and imaging. Currently, a filter that suppresses 100MHz and its harmonics is in use. Development of a MR-compatible hyperthermia applicator for head and neck requires a filter that can suppress also the 433.92MHz signal. A unique new filter which has high power handling, extremely high suppression, and selectivity has been designed that attenuates 100MHz and 433.92MHz signals with low insertion loss (&lt;0.25dB) at 63.89MHz. 0.14dB insertion loss at 63.89MHz, 112dB, 88dB and 93dB signal attenuation were achieved at 100MHz, 200MHz and 433.92MHz, respectively, with the new filter design using model of LM-500 cable. A proof of concept filter was constructed to validate the design. Our investigation shows that filter requirements can be satisfied, but high-power low-loss coaxial cables are necessary.</p

Exploiting Polynomial Chaos Expansion for Rapid Assessment of the Impact of Tissue Property Uncertainties in Low-Intensity Focused Ultrasound Stimulation

Neuromodulation with low-intensity focused ultrasound (LIFUS) holds significant promise for noninvasive treatment of neurological disorders, but its success relies heavily on accurately targeting specific brain regions. Computational model predictions can be used to optimize LIFUS, but uncertain acoustic tissue properties can affect prediction accuracy. The Monte Carlo method is often used to quantify the impact of uncertainties, but many iterations are generally needed for accurate estimates. We studied a surrogate model based on polynomial chaos expansion (PCE) to quantify the uncertainty in the LIFUS acoustic intensity field caused by tissue acoustic property uncertainties. The PCE approach was benchmarked against Monte Carlo method for LIFUS in three different head models. We also investigated the effect of the number of PCE samples on the accuracy of the surrogate model. Our results show that the PCE surrogate model requires only 20 simulation samples to estimate the mean and standard deviation of the acoustic intensity field with high accuracy compared to 100 samples needed for Monte Carlo method. The root mean squared percentage error (RMSPE) in the mean acoustic intensity field was less than 1.5%, with a maximum error of less than 0.5 W/cm2 (&lt; 1% of the focus peak intensity in water), while the RMSPE in the standard deviation was less than 9%, with a maximum error of less than 0.3 W/cm2. The accuracy of the PCE surrogate model, and the limited number of iterations it requires makes it a promising tool for quantifying the uncertainty in the acoustic intensity field in LIFUS applications.</p

The potential of adjusting water bolus liquid properties for economic and precise MR thermometry guided radiofrequency hyperthermia

The potential of MR thermometry (MRT) fostered the development of MRI compatible radiofrequency (RF) hyperthermia devices. Such device integration creates major technological challenges and a crucial point for image quality is the water bolus (WB). The WB is located between the patient body and external sources to both couple electromagnetic energy and to cool the patient skin. However, the WB causes MRT errors and unnecessarily large field of view. In this work, we studied making the WB MRI transparent by an optimal concentration of compounds capable of modifying T2 * relaxation without an impact on the efficiency of RF heating. Three different T2 * reducing compounds were investigated, namely CuSO4, MnCl2, and Fe3 O4. First, electromagnetic properties and T2 * relaxation rates at 1.5 T were measured. Next, through multi-physics simulations, the predicted effect on the RF-power deposition pattern was evaluated and MRT precision was experimentally assessed. Our results identified 5 mM Fe3 O4 solution as optimal since it does not alter the RF-power level needed and improved MRT precision from 0.39◦ C to 0.09◦ C. MnCl2 showed a similar MRT improvement, but caused unacceptable RF-power losses. We conclude that adding Fe3 O4 has significant potential to improve RF hyperthermia treatment monitoring under MR guidance

Adapting temperature predictions to MR imaging in treatment position to improve simulation-guided hyperthermia for cervical cancer

Hyperthermia treatment consists of elevating the temperature of the tumor to increase the effectiveness of radiotherapy and chemotherapy. Hyperthermia treatment planning (HTP) is an important tool to optimize treatment quality using pre-treatment temperature predictions. The accuracy of these predictions depends on modeling uncertainties such as tissue properties and positioning. In this study, we evaluated if HTP accuracy improves when the patient is imaged inside the applicator at the start of treatment. Because perfusion is a major uncertainty source, the importance of accurate treatment position and anatomy was evaluated using different perfusion values. Volunteers were scanned using MR imaging without (&amp;#x201C;planning setup&amp;#x201D;) and with the MR-compatible hyperthermia device (&amp;#x201C;treatment setup&amp;#x201D;). Temperature-based quality indicators were used to assess the differences between the standard, apparent and the optimized hyperthermia dose. We conclude that pre-treatment imaging can improve HTP predictions accuracy but also, that tissue perfusion modelling is crucial if temperature-based optimization is applied.</p

ESHO benchmarks for computational modeling and optimization in hyperthermia therapy

Background: The success of cancer hyperthermia (HT) treatments is strongly dependent on the temperatures achieved in the tumor and healthy tissues as it correlates with treatment efficacy and safety, respectively. Hyperthermia treatment planning (HTP) simulations have become pivotal for treatment optimization due to the possibility for pretreatment planning, optimization and decision making, as well as real-time treatment guidance. Materials and methods: The same computational methods deployed in HTP are also used for in silico studies. These are of great relevance for the development of new HT devices and treatment approaches. To aid this work, 3 D patient models have been recently developed and made available for the HT community. Unfortunately, there is no consensus regarding tissue properties, simulation settings, and benchmark applicators, which significantly influence the clinical relevance of computational outcomes. Results and discussion: Herein, we propose a comprehensive set of applicator benchmarks, efficacy and safety optimization algorithms, simulation settings and clinical parameters, to establish benchmarks for method comparison and code verification, to provide guidance, and in view of the 2021 ESHO Grand Challenge (Details on the ESHO grand challenge on HTP will be provided at https://www.esho.info/). Conclusion: We aim to establish guidelines to promote standardization within the hyperthermia community such that novel approaches can quickly prove their benefit as quickly as possible in clinically relevant simulation scenarios. This paper is primarily focused on radiofrequency and microwave hyperthermia but, since 3 D simulation studies on heating with ultrasound are now a reality, guidance as well as a benchmark for ultrasound-based hyperthermia are also included

Standardization of patient modeling in hyperthermia simulation studies: introducing the Erasmus Virtual Patient Repository

Purpose: Thermal dose-effect relations have demonstrated that clinical effectiveness of hyperthermia would benefit from more controlled heating of the tumor. Hyperthermia treatment planning (HTP) is a potent tool to study strategies enabling target conformal heating, but its accuracy is affected by patient modeling approximations. Homogeneous phantoms models are being used that do not match the body shape of patients in treatment position and often have unrealistic target volumes. As a consequence, simulation accuracy is affected, and performance comparisons are difficult. The aim of this study is to provide the first step toward standardization of HTP simulation studies in terms of patient modeling by introducing the Erasmus Virtual Patient Repository (EVPR): a virtual patient model database.Methods: Four patients with a tumor in the head and neck or the pelvis region were selected, and corresponding models were created using a clinical segmentation procedure. Using the Erasmus University Medical Center standard procedure, HTP was applied to these models and compared to HTP for commonly used surrogate models.Results: Although this study was aimed at presenting the EVPR database, our study illustrates that there is a non-negligible difference in the predicted SAR patterns between patient models and homogeneous phantom-based surrogate models. We further demonstrate the dif

Feasibility and relevance of discrete vasculature modeling in routine hyperthermia treatment planning

Purpose: To investigate the effect of patient specific vessel cooling on head and neck hyperthermia treatment planning (HTP). Methods and materials: Twelve patients undergoing radiotherapy were scanned using computed tomography (CT), magnetic resonance imaging (MRI) and contrast enhanced MR angiography (CEMRA). 3D patient models were constructed using the CT and MRI data. The arterial vessel tree was constructed from the MRA images using the ‘graph-cut’ method, combining information from Frangi vesselness filtering and region growing, and the results were validated against manually placed markers in/outside the vessels. Patient specific HTP was performed and the change in thermal distribution prediction caused by arterial cooling was evaluated by adding discrete vasculature (DIVA) modeling to the Pennes bioheat equation (PBHE). Results: Inclusion of arterial cooling showed a relevant impact, i.e., DIVA modeling predicts a decreased treatment quality by on average 0.19 °C (T90), 0.32 °C (T50) and 0.35 °C (T20) that is robust against variations in the inflow blood rate (|ΔT| 0.5 °C) were observed. Conclusion: Addition of patient-specific DIVA into the thermal modeling can significantly change predicted treatment quality. In cases where clinically detectable vessels pass the heated region, we advise to perform DIVA modeling

Data for: Patient-derived breast model repository, a tool for hyperthermia treatment planning and applicator design

Developed at Erasmus University Medical Center, the Breast Tumor Patient Models (BTPM) Repository consists of 22 unique breast models that are segmented into six tissues including a tumor tissue [1]. The main goal of this repository is to create a unique platform to advance the field of intact breast hyperthermia by facilitating an objective comparison of the heating quality for different hyperthermia devices using the same set of patient-derived models. The models in this repository offer a large variability of anatomical and pathological characteristics in the intact breast. Two of the models (Models 4 and 20) are also part of the ESHO benchmarks for computational modeling and optimization in hyperthermia therapy [2]. In terms of anatomical characteristics, the breast models range from small volumes (154 ml) to large volumes (1336 ml) and a mean value (591 ml). Another important anatomical characteristic, the breast density, is well covered, with three out of four breast density groups included in this database. The missing type I (almost entirely fat) breast density category is known to have a significantly lower risk of developing breast cancer, and hence does not appear in this database. In terms of pathological characteristics, all the repository models hold important variations in terms of tumor size, tumor location, and tumor depth. In terms of tumor size, the tumor volumes vary from very small 1.8 ml to 39 ml. The vast majority of tumors are stage T2 (20–50mm) tumors (18/22), but both T1 (50 mm; 2/22) staged tumors are also present in this repository. In terms of tumor location, the current repository covers all the areas of the breast, with the most common tumor site being the upper outer region. In terms of tumor depth, the tumor sites cover all ranges from superficially positioned tumors to tumors very close to the breast base, close to the chest wall. This is evident in the large variation of tumor center to skin distance, varying from 12 to 43 mm, with an average of 26 mm. All models in the BPTM Repository were created from fully anonymized contrast enhanced MRI data of the breast cancer patients undergoing neoadjuvant chemotherapy. All models were segmented into six tissues: skin, bone, muscle, tumor, fibroglandular, and fat. Skin, thoracic bones, pectoralis major muscle, and the tumor lesion were manually contoured. The remaining tissue volume was segmented into fatty and fibroglandular tissue using an automatic segmentation method based on the voxel intensity described in Zastrow et al. [3] and Omer et al. [4]

The Required Patient Modeling Realism in Radiofrequency Heating Simulation Studies

Clinical effectiveness of hyperthermia would benefit from a more controlled and target conformal heating of the tumor. Over the years, dosimetry using electromagnetic simulators has become a potent tool to study improvements in the application of hyperthermia. Literature suggests that simulation accuracy is dependent on the realism of the patient models. In this work, we compare the results for a detailed head and neck patient model to those for models with an approximated shape, a reduced tissue number and/or a spherical target volume. Our comparison shows a relative difference above 25% in the administered power absorption pattern. This large difference calls upon 1) follow-up research to establish the true impact using a larger set of patient models and 2) the development of a reference set of patient models to facilitate benchmarking of novel devices, methods and treatment approaches