Abstract #2550

Iterative Compressed Sensing Reconstruction for 3D Non-Cartesian Trajectories Without Gridding & Regridding at Every Iteration

Mehmet Akcakaya*¹, Seunghoon Nam*^1,2, Tamer Basha¹, Vahid Tarokh², Warren J. Manning¹, Reza Nezafat¹

¹Dept. of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; ²School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, United States

3D non-Cartesian sampling trajectories allow high isotropic spatial resolution, better depiction of cardiac anatomy and ease of image prescription in cardiac MRI. Furthermore, undersampling with these trajectories causes incoherent artifacts that may be removed using compressed sensing (CS). CS reconstruction is typically done using conjugate-gradient (CG) type algorithms, which require gridding and regridding to be performed at every iteration. In this abstract, we investigate an alternative method for CS reconstruction that only requires two gridding and one regridding operation in total irrespective of the number of iterations.