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Abstract #3851

MRSI Brain Temperature Mapping Using Machine Learning

Dhritiman Das1,2,3,4, Michael J Thrippleton3, Scott IK Semple5, Rolf F Schulte4, Mike E Davies2, Bjoern H Menze1, and Ian Marshall3

1Department of Computer Science, Technical University of Munich, Munich, Germany, 2Institute for Digital Communications, University of Edinburgh, Edinburgh, Scotland, 3Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, 4GE Healthcare, Munich, Germany, 5Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, Scotland

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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