MR Thermometry with a Deep-Learning Reconstruction

Motivation: Improved MR thermometry is needed for thermal-based therapies. Goal(s): Improve precision while maintaining accuracy by denoising proton resonance frequency shift (PRFS)-based thermometry images using a deep learning-based reconstruction (DLR). Approach: 2D fast spoiled gradient-echo (FSPGR) images were acquired on a variety of phantoms with and without heating. Complex images were reconstructed with and without DLR to calculate temperature change maps. The mean and standard deviation of ROIs were analyzed to demonstrate any changes in accuracy and precision. Results: DLR improves precision and maintains accuracy in PRFS temperature change maps in phantoms. Impact: The improvements indicate an opportunity to increase spatial and/or temporal resolution in MRI thermometry. It may be possible to improve MRI-based heating optimization during clinical thermal therapies.