PURPOSE: 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. METHODS: 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 B 0 -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. RESULTS: Simulations and phantom heating experiments revealed that using BFR or TFI preserves temperature-induced phase change, while removing susceptibility artifacts and B 0 -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. CONCLUSION: 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.      «

PURPOSE: 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. METHODS: 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...     »