We propose a machine-learning framework for brain temperature estimation in MRSI using human in-vivo data from 1.5T and 3T scanners. We consider the chemical-shift based method as our benchmark and compare our results against it. Our framework, based on random-forest regression, performs a K-fold cross validation on the MRSI dataset which includes (1) learning the spectral features (including the chemical-shift) from the subjects; (2) obtaining brain temperature estimates and computing the error over the corresponding jMRUI-fitted chemical-shift based estimates. Compared to jMRUI, our method, after training, gives a low estimation error and a 30-fold improvement in estimation speed per patient.
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