Back to Search Start Over

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

Authors :
VilasBoas-Ribeiro, Iva (author)
Nouwens, Sven A.N. (author)
Curto, Sergio (author)
Jager, Bram de (author)
Franckena, Martine (author)
van Rhoon, G.C. (author)
Heemels, W. P.M.H. (author)
Paulides, Margarethus M. (author)
VilasBoas-Ribeiro, Iva (author)
Nouwens, Sven A.N. (author)
Curto, Sergio (author)
Jager, Bram de (author)
Franckena, Martine (author)
van Rhoon, G.C. (author)
Heemels, W. P.M.H. (author)
Paulides, Margarethus M. (author)
Publication Year :
2022

Abstract

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 ma<br />RST/Applied Radiation & Isotopes

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1357878780
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1002.mp.15811