WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in $$ {}^1\mathrm{H} $$ MR spectroscopic imaging.

ABSTRACT:

Purpose

Proton magnetic resonance spectroscopic imaging (  1H$$ {}^1\mathrm{H} $$ -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain  1H$$ {}^1\mathrm{H} $$ -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution  1H$$ {}^1\mathrm{H} $$ -MRSI to accurately remove lipid and water signals while preserving the metabolite signal. The potential of supervised neural networks for this task remains unexplored, despite their success for other MRSI processing.

Methods

We introduce a deep learning method based on a modified Y-NET network for water and lipid removal in whole-brain  1H$$ {

SUBMITTER: Weiser PJ 

PROVIDER: S-EPMC11782715 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature