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Abstract #3631

A Deconvolution Method for Improved CBF Quantification in 3D-GRASE ASL

Kenneth Wengler1,2 and Xiang He2

1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 2Radiology, Stony Brook University, Stony Brook, NY, United States

Pseudo-continuous arterial spin labeling (pCASL) with segmented 3D-GRASE acquisition is widely accepted as the optimal ASL technique. However, the method suffers from blurring along the partition direction caused by point spread function (PSF) broadening. In this study, a PSF deconvolution method for pCASL images with 3D-GRASE acquisition is developed and evaluated in simulations and in-vivo experiments. The deconvolution method greatly reduces the effects of the PSF and recover the perfusion signal for segmentation factors of at least 2PAR x 2PE. The proposed deconvolution method improves the accuracy of cerebral blood flow quantification and facilitates the use of lower segmentation factors.

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