The efficacy of a hyperthermia treatment depends on the delivery of well-controlled heating; hence, accurate temperature monitoring is essential for ensuring effective treatment. For deep pelvic hyperthermia, there are no comprehensive and systematic reports on MR thermometry. Moreover, data inclusion generally lacks objective selection criteria leading to a high probability of bias when comparing results. Herein, we studied whether imaging-based data inclusion predicts accuracy and could serve as a tool for prospective patient selection. The accuracy of the MR thermometry in patients with locally advanced cervical cancer was benchmarked against intraluminal temperature. We found that gastrointestinal air motion at the start of the treatment, quantified by the Jaccard similarity coefficient, was a good predictor for MR thermometry accuracy. The results for the group that was selected for low gastrointestinal air motion improved compared to the results for all patients by 50% (accuracy), 26% (precision), and 80% (bias). We found an average MR thermometry accuracy of 2.0 °C when all patients were considered and 1.0 °C for the selected group. These results serve as the basis for comprehensive benchmarking of novel technologies. The Jaccard similarity coefficient also has good potential to prospectively determine in which patients the MR thermometry will be valuable.@en

Purpose: We studied the differences between planning and treatment position, their impact on the accuracy of hyperthermia treatment planning (HTP) predictions, and the relevance of including true treatment anatomy and position in HTP based on magnetic resonance (MR) images. Materials and methods: All volunteers were scanned with an MR-compatible hyperthermia device, including a filled waterbolus, to replicate the treatment setup. In the planning setup, the volunteers were scanned without the device to reproduce the imaging in the current HTP. First, we used rigid registration to investigate the patient position displacements between the planning and treatment setup. Second, we performed HTP for the planning anatomy at both positions and the treatment mimicking anatomy to study the effects of positioning and anatomy on the quality of the simulated hyperthermia treatment. Treatment quality was evaluated using SAR-based parameters. Results: We found an average displacement of 2 cm between planning and treatment positions. These displacements caused average absolute differences of ∼12% for TC25 and 10.4%–15.9% in THQ. Furthermore, we found that including the accurate treatment position and anatomy in treatment planning led to an improvement of 2% in TC25 and 4.6%–10.6% in THQ. Conclusions: This study showed that precise patient position and anatomy are relevant since these affect the accuracy of HTP predictions. The major part of improved accuracy is related to implementing the correct position of the patient in the applicator. Hence, our study shows a clear incentive to accurately match the patient position in HTP with the actual treatment.@en

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 ('planning setup') and with the MR-compatible hyperthermia device ('treatment setup'). 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.@en

Background: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose: This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods: The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results: First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the match between temperature prediction and temperature acquired by intraluminal thermometry for patients treated for locally advanced cervical cancer. Considering all patients, the POD–Kalman filter improved MAE by 43% (filtered MR thermometry = 1.29°C, POD–Kalman filtered temperature = 0.74°C). Moreover, the SDE was improved by 47% (filtered MR thermometry = 1.16°C, POD–Kalman filtered temperature = 0.61°C). Specifically, the POD–Kalman filter reduced the MAE by approximately 60% in patients whose MR thermometry was unreliable because of the great amount of susceptibilities caused by the high level of intestinal gas motion. Conclusions: We showed that the POD–Kalman filter significantly improved the accuracy of temperature monitoring compared to MR thermometry in heating experiments and hyperthermia treatments. The results demonstrated that POD–Kalman filtering can improve thermal dosimetry during RF hyperthermia treatment, especially when MR thermometry is inaccurate.@en

In hyperthermia, the general opinion is that pre-treatment optimization of treatment settings requires a patient-specific model. For deep pelvic hyperthermia treatment planning (HTP), tissue models comprising four tissue categories are currently discriminated. For head and neck HTP, we found that more tissues are required for increasing accuracy. In this work, we evaluated the impact of the number of segmented tissues on the predicted specific absorption rate (SAR) for the pelvic region. Highly detailed anatomical models of five healthy volunteers were selected from a virtual database. For each model, seven lists with varying levels of segmentation detail were defined and used as an input for a modeling study. SAR changes were quantified using the change in target-to-hotspot-quotient and maximum SAR relative differences, with respect to the most detailed patient model. The main finding of this study was that the inclusion of high water content tissues in the segmentation may result in a clinically relevant impact on the SAR distribution and on the predicted hyperthermia treatment quality when considering our pre-established thresholds. In general, our results underline the current clinical segmentation protocol and help to prioritize any improvements.@en