Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.

ABSTRACT:

Purpose

A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning-based FPC.

Methods

Two novel deep learning-based FPC methods (deep learning-based Cr referencing and deep learning-based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency.

SUBMITTER: Shamaei A 

PROVIDER: S-EPMC10098589 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature