Obstructive sleep apnea (OSA) affects 10% of the population, and is associated with brain injury. Neurochemical changes in the brain of OSA patients can be recorded using 5D echo-planar J-resolved spectroscopic imaging. Accelerated acquisition is achieved with non-uniform undersampling, which requires data reconstruction that can be done with compressed sensing (CS). We implemented CS with a hybrid DLTV reconstruction method combining dictionary learning (DL) and total variation (TV), and compared its performance with Perona-Malik (PM) reconstruction. The metabolite ratios were consistent with both DLTV and PM while DLTV recovered the metabolite peaks near residual water better in many voxels.
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