Keywords: Data Processing, Spectroscopy, Optimization, Torch, Auto-differentiation.
Motivation: This study addresses the time-consuming water residual removal process in MRSI, aiming to expedite it, given the lack of GPU implementation for methods like HLSVD-PRO.
Goal(s): We aim to increase water residual removal speed using Torch for GPU-parallelized linear combination model fitting.
Approach: Our method utilizes Torch to model and optimize water residuals fitting with 7-Lorentzian profiles. Performance is evaluated with in a large in-vivo dataset, comparing our method to HLSVD-PRO both in efficiency and accuracy.
Results: Our approach accelerates water residual removal, outperforming HLSVD-PRO in processing speed by a factor 14x, while mantaining equivalent quantification accuracy, offering promise for MRSI applications.
Impact: This study's accelerated water residual removal method in MRSI can benefit scientists and clinicians by reducing processing time. And it opens avenues for more extensive research in the topic.
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