Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning

Background Deep learning‐based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning‐based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR). Purpose To investigate a convolutional neural network‐based SR (CNN‐SR) approach for simultaneous frequency‐and‐phase correction (FPC) of single‐voxel Meshcher–Garwood point‐resolved spectroscopy (MEGA‐PRESS) MRS data. Study Type Retrospective. Subjects Forty thousand simulated MEGA‐PRESS datasets generated from FID Appliance (FID‐A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA‐PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets. Field Strength/Sequence 3T, MEGA‐PRESS. Assessment Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were −20 to 20 Hz and −90° to 90° and were uniformly distributed for the simulation dataset at different signal‐to‐noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0–5 Hz; 0–20°), medium offsets (5–10 Hz; 20–45°), and large offsets (10–20 Hz; 45–90°). Statistical Tests Two‐tailed paired t‐tests for model performances in the simulation and in vivo datasets were used and a P‐value