Gradient scheme optimization for PRESS-localized edited MRS using weighted pathway suppression.

This study aimed to design and implement an optimized gradient scheme for PRESS-localized edited magnetic resonance spectroscopy (MRS) to enhance suppression of out-of-voxel (OOV) artifacts. These artifacts, which originate from insufficient crushing of unwanted coherence transfer pathways (CTPs), are particularly challenging in editing schemes for metabolites like gamma-aminobutyric acid (GABA) and glutathione (GSH). To address this, a volume-based likelihood model was developed to guide gradient scheme optimization, prioritizing suppression of CTPs based on likelihood. A volume-based likelihood model for CTP weighting was integrated into a Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient optimization. Using a genetic algorithm with a new dual-penalty cost function, gradient schemes were optimized to maximize pathway-specific suppression. Hardware and sequence constraints, maximum gradient amplitudes and delay durations respectively, informed the optimization. Validation of the optimized scheme was performed with simulations and using an edited sequence in three brain regions (posterior cingulate cortex PCC, thalamus, and medial prefrontal cortex mPFC), with particular focus on OOV artifact reduction and spectral quality improvements. The optimized gradient scheme demonstrated improved k-space crushing efficiency (by an average of 197%). OOV artifacts were reduced in all brain regions, particularly in highly OOV-susceptible regions (thalamus and mPFC). Improvements were most notable around 4.3 ppm with significant OOV amplitude reductions (p < 0.001). By using a volume-based likelihood model for CTP prioritization, the optimized scheme ensures robust and region-agnostic performance in reducing OOV artifacts.