Application of a ¹H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

ABSTRACT: Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic ¹H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A

SUBMITTER: Gudmundson AT 

PROVIDER: S-EPMC10197548 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature