Boehm Christof, Goeger-Neff Marianne, Mulder Hendrik T, Zilles Benjamin, Lindner Lars H, van Rhoon Gerard C, Karampinos Dimitrios C, Wu Mingming
Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.
Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.
Magn Reson Med. 2022 Jul;88(1):120-132. doi: 10.1002/mrm.29191. Epub 2022 Mar 21.
MR temperature monitoring of mild radiofrequency hyperthermia (RF-HT) of cancer exploits the linear resonance frequency shift of water with temperature. Motion-induced susceptibility distribution changes cause artifacts that we correct here using the total field inversion (TFI) approach.
The performance of TFI was compared to two background field removal (BFR) methods: Laplacian boundary value (LBV) and projection onto dipole fields (PDF). Data sets with spatial susceptibility change and -drift were simulated, phantom heating experiments were performed, four volunteer data sets at thermoneutral conditions as well as data from one cervical cancer, two sarcoma, and one seroma patients undergoing mild RF-HT were corrected using the proposed methods.
Simulations and phantom heating experiments revealed that using BFR or TFI preserves temperature-induced phase change, while removing susceptibility artifacts and -drift. TFI resulted in the least cumulative error for all four volunteers. Temperature probe information from four patient data sets were best depicted by TFI-corrected data in terms of accuracy and precision. TFI also performed best in case of the sarcoma treatment without temperature probe.
TFI outperforms previously suggested BFR methods in terms of accuracy and robustness. While PDF consistently overestimates susceptibility contribution, and LBV removes valuable pixel information, TFI is more robust and leads to more accurate temperature estimations.
癌症轻度射频热疗(RF-HT)的磁共振温度监测利用了水的线性共振频率随温度的变化。运动引起的磁化率分布变化会导致伪影,我们在此使用总场反演(TFI)方法进行校正。
将TFI的性能与两种背景场去除(BFR)方法进行比较:拉普拉斯边界值(LBV)和偶极场投影(PDF)。模拟了具有空间磁化率变化和漂移的数据集,进行了体模加热实验,使用所提出的方法对四个热中性条件下的志愿者数据集以及一名宫颈癌、两名肉瘤和一名血清肿患者在轻度RF-HT治疗过程中的数据进行了校正。
模拟和体模加热实验表明,使用BFR或TFI可以保留温度引起的相位变化,同时去除磁化率伪影和漂移。对于所有四名志愿者,TFI导致的累积误差最小。就准确性和精确性而言,来自四个患者数据集的温度探头信息在TFI校正后的数据中得到了最佳呈现。在没有温度探头的肉瘤治疗中,TFI的表现也最佳。
在准确性和稳健性方面,TFI优于先前提出的BFR方法。虽然PDF始终高估磁化率贡献,而LBV会去除有价值的像素信息,但TFI更稳健,能带来更准确的温度估计。