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

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

Quantitative, Multi-institutional Evaluation of MR Thermometry Accuracy for Deep-Pelvic MR-Hyperthermia Systems Operating in Multi-vendor MR-systems Using a New Anthropomorphic Phantom

Clinical outcome of hyperthermia depends on the achieved target temperature, therefore target conformal heating is essential. Currently, invasive temperature probe measurements are the gold standard for temperature monitoring, however, they only provide limited sparse data. In contrast, magnetic resonance thermometry (MRT) provides unique capabilities to non-invasively measure the 3D-temperature. This study investigates MRT accuracy for MR-hyperthermia hybrid systems located at five European institutions while heating a centric or eccentric target in anthropomorphic phantoms with pelvic and spine structures. Scatter plots, root mean square error (RMSE) and Bland–Altman analysis were used to quantify accuracy of MRT compared to high resistance thermistor probe measurements. For all institutions, a linear relation between MRT and thermistor probes measurements was found with R 2 (mean ± standard deviation) of 0.97 ± 0.03 and 0.97 ± 0.02, respectively for centric and eccentric heating targets. The RMSE was found to be 0.52 ± 0.31 ◦C and 0.30 ± 0.20 ◦C, respectively. The Bland-Altman evaluation showed a mean difference of 0.46 ± 0.20 ◦C and 0.13 ± 0.08 ◦C, respectively. This first multi-institutional evaluation of MR-hyperthermia hybrid systems indicates comparable device performance and good agreement between MRT and thermistor probes measurements. This forms the basis to standardize treatments in multi-institution studies of MR-guided hyperthermia and to elucidate thermal dose-effect relations

The potential of constrained SAR focusing for hyperthermia treatment planning: analysis for the head & neck region

Clinical trials have shown that hyperthermia is a potent adjuvant to conventional cancer treatments, but the temperatures currently achieved in the clinic are still suboptimal. Hyperthermia treatment planning simulations have potential to improve the heating profile of phased-array applicators. An important open challenge is the development of an effective optimization procedure that enables uniform heating of the target region while keeping temperature below a threshold in healthy tissues. In this work, we analyzed the effectiveness and efficiency of a recently proposed optimization approach, i.e. focusing via constrained power optimization (FOCO), using 3D simulations of twelve clinical patient specific models. FOCO performance was compared against a clinically used particle swarm based optimization approach. Evaluation metrics were target coverage at the 25% iso-SAR level, target hotspot quotient, median target temperature (T50) and computational requirements. Our results show that, on average, constrained power focusing performs slightly better than the clinical benchmark (ΔT50 = +0.05 °C), but outperforms this clinical benchmark for large target volumes (>40 cm 3 , Δ T50 = +0.39 °C). In addition, the results are achieved in a shorter time (-44%) and are repeatable because the approach is formulated as a convex optimization problem