Accelerated water residual removal in <scp>MRS</scp> : Exploring deep learning versus fitting‐based approaches

WaterFit showed a superior balance of accuracy and processing speed in removing water residual from MRS data compared to DeepWatR. The proposed WaterFit implementation significantly reduces preprocessing time while maintaining metabolite fitting accuracy comparable to gold standard methods. This advancement addresses the need for efficient processing methods that can facilitate analysis and enhance the clinical utility of MRS.

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Publication Details

Journal
Magnetic Resonance in Medicine
Published
2025-09-02
DOI
https://doi.org/10.1002/mrm.70031
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Accelerated water residual removal in MRS : Exploring deep learning versus fitting‐based approaches

Johannes Slotboom, Milena Capiglioni, Federico Turco
Magnetic Resonance in Medicine
Advanced MRI Techniques and Applications
article

Accelerated water residual removal in MRS : Exploring deep learning versus fitting‐based approaches

Johannes Slotboom, Milena Capiglioni, Federico Turco
article en

Abstract

WaterFit showed a superior balance of accuracy and processing speed in removing water residual from MRS data compared to DeepWatR. The proposed WaterFit implementation significantly reduces preprocessing time while maintaining metabolite fitting accuracy comparable to gold standard methods. This advancement addresses the need for efficient processing methods that can facilitate analysis and enhance the clinical utility of MRS.

Magnetic Resonance in MedicineVol. 95(1)
University of Bern (CH)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, H2020 Marie Skłodowska-Curie Actions
Openalex Percentile: Top 68%
Advanced MRI Techniques and Applications
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