Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting.

ABSTRACT:

Purpose

To propose a novel end-to-end deep learning model to quantify absolute metabolite concentrations from in vivo J-point resolved spectroscopy (JPRESS) without using spectral fitting.

Methods

A novel encoder-decoder-style neural network was created, which was trained to predict metabolite concentrations and individual component signals concurrently from 3T JPRESS data in the time domain. The training data set contained 100 000 samples created by spin-density simulations using experimentally used RF pulses. Concentrations, phase, frequencies, linewidths, and T₂ relaxation times in the training data set were varied over a large range with uniform distributions. Random synthesized noise and extraneous signals were added to the data set. Two thousand validation s

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10524908 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature