Abstract #3433

MRS Quantification using Deep Learning Frameworks: an Accuracy and Efficiency Study

Federico Turco¹ and Johannes Slotboom²

¹Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland, ²Support Center for Advanced Neuroimaging (SCAN), Institute of Diagnostic and Interventional Neuroradiology, Bern, Switzerland

Synopsis

Keywords: Data Processing, Spectroscopy, Deep learning frameworks, Deep learning, OptimizationWe implemented a model fitting algorithm for magnetic resonance spectroscopy quantification using Pytorch. This network fit the spectra by minimizing the error between the spectrum modeled by the newtwork and the desired target. The implementation can fit multiple spectras in parallel using Pytorch GPU acceleration. We compared the results with a parallel version of TDFDFit and found it to be up to 12 time faster fitting 2048 spectras in 50 seconds.

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