Radiology 2021 Jun 15:202624. Epub 2021 Jun 15.
From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.).
Background Obtaining ventricular volumetry and mass is key to most cardiac MRI but challenged by long multibreath-hold acquisitions. Purpose To assess the image quality and performance of a highly accelerated, free-breathing, two-dimensional cine cardiac MRI sequence incorporating deep learning (DL) reconstruction compared with reference standard balanced steady-state free precession (bSSFP). Materials and Methods A DL algorithm was developed to reconstruct custom 12-fold accelerated bSSFP cardiac MRI cine images from coil sensitivity maps using 15 iterations of separable three-dimensional convolutions and data consistency steps. Read More