Keywords: Spectroscopy, Spectroscopy, Machine Learning/Artificial Intelligence, Artifacts, Data Processing, Software Tools, Simulations, Brain, Pediatric
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.
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