Quality Assessment Tool using Deep Learning for GABA-Edited MRS data

Motivation: GABA-edited MRS suffers from data quality challenges due to its low signal to noise ratio (SNR). Goal(s): We propose an automated labeling algorithm for transient quality and a dual-domain deep learning model for filtering spectra transients based on quality. Approach: We trained our model with simulated data containing commonly occurring artifacts labelled with our continuous automated labelling algorithm which ranges from –1 (poor quality) to +1 (good quality). We subsequently evaluated our model’s performance by removing (filtering) poor quality transients corresponding to quality values less than 0. Results: Our model outperformed qualitatively simple averaging using all collected transients for 70-80% of scans. Impact: Our model can successfully assign a continuous quality score between –1 (poor) and +1 (good) to GABA-edited MRS difference data (i.e., a single ON-OFF edit pair) which when used for filtering, improves MRS quality metrics compared to simple transient averaging.