POD–Kalman filtering for improving noninvasive 3D temperature monitoring in MR-guided hyperthermia

POD–Kalman filtering for improving noninvasive 3D temperature monitoring in MR-guided hyperthermia

VilasBoas-Ribeiro, Iva (Erasmus MC)
Nouwens, Sven A.N. (Eindhoven University of Technology)
Curto, Sergio (Erasmus MC)
Jager, Bram de (Eindhoven University of Technology)
Franckena, Martine (Erasmus MC)
van Rhoon, G.C. (TU Delft RST/Applied Radiation & Isotopes; Erasmus MC)
Heemels, W. P.M.H. (Eindhoven University of Technology)
Paulides, Margarethus M. (Erasmus MC; Eindhoven University of Technology)

2022

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.

hyperthermia
Kalman filter
MR thermometry
proper orthogonal decomposition
thermotherapy

http://resolver.tudelft.nl/uuid:20cb5d6a-db70-4335-8c58-32c4b79ebf89

https://doi.org/10.1002/mp.15811

0094-2405

Medical Physics, 49 (8), 4955-4970

Institutional Repository

journal article

© 2022 Iva VilasBoas-Ribeiro, Sven A.N. Nouwens, Sergio Curto, Bram de Jager, Martine Franckena, G.C. van Rhoon, W. P.M.H. Heemels, Margarethus M. Paulides