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A model-based reconstruction framework is proposed for MOtion-Corrected andHigh-resolution anatomically-Assisted (MOCHA) reconstruction of ASL data. Inthis framework, all low-resolution ASL control-label pairs are used toreconstruct a single high-resolution cerebral blood flow (CBF) map, correctedfor rigid motion, point-spread-function (PSF) blurring and partial-volumeeffect (PVE).Six volunteers were recruited for CBF imaging using PCASLlabelling, 2-shot 3D-GRASE sequences and high-resolution T1-weighted MRI. Fortwo volunteers, high-resolution scans with double and triple resolution in thepartition direction were additionally collected. Simulations were designed forevaluations against a high-resolution ground-truth CBF map, including asimulated hyper-perfused lesion and hyper/hypo-perfusion abnormalities. MOCHAwas compared to standard reconstruction and a 3D linear regression (3DLR) PVEcorrection method and was further evaluated for acquisitions with reducedcontrol-label pairs and k-space undersampling. MOCHA reconstructions oflow-resolution ASL data showed enhanced image quality particularly in thepartition direction. In simulations, both MOCHA and 3DLR provided more accurateCBF maps than the standard reconstruction, however MOCHA resulted in the lowesterrors and well delineated the abnormalities. MOCHA reconstruction ofstandard-resolution in-vivo data showed good agreement with higher-resolutionscans requiring 4x and 9x longer acquisitions. MOCHA was found to be robust for4x-accelerated ASL acquisitions, achieved by reduced control-label pairs ork-space undersampling. Conclusion: MOCHA reconstruction reduces PVE by directreconstruction of CBF maps in the high-resolution space of the correspondinganatomical image, incorporating motion correction and PSF modelling. Followingfurther evaluation, MOCHA should promote the clinical application of ASL.

Motion-corrected and high-resolution anatomically-assisted (MOCHA) reconstruction of arterial spin labelling MRI