Stephen F. Cauley1, Kawin Setsompop1, 2, Jonathan R. Polimeni1, 2, Lawrence L. Wald1, 3
1A. A. Martinos Center for Biomedical Imaging, Dept. of Radiology, MGH, Charlestown, MA, United States; 2Harvard Medical School, Boston, MA, United States; 3Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States
For full-brain coverage simultaneously acquiring multiple slices can significantly improve acquisition time. Controlling the unaliasing process is extremely important given the presence of noise and artifacts in fMRI and diffusion. In this work we test a constrained optimization technique that reduces inter-slice artifacts by more than 10 fold. The improved kernels can be used in both fMRI and diffusion weighting studies for increased accuracy. The convex model ensures optimal solutions given the constraints and is implemented easily using readily available optimization packages.
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