End‐to‐end deep learning nonrigid motion‐corrected reconstruction for highly accelerated free‐breathing coronary MRA. Issue 4 (6th June 2021) - Qi, Haikun; Hajhosseiny, Reza; Cruz, Gastao; Kuestner, Thomas; Kunze, Karl; Neji, Radhouene; Botnar, René; Prieto, Claudia
Abstract : Purpose: To develop an end‐to‐end deep learning technique for nonrigid motion‐corrected (MoCo) reconstruction of ninefold undersampled free‐breathing whole‐heart coronary MRA (CMRA). Methods: A novel deep learning framework was developed consisting of a diffeomorphic registration network and a motion‐informed model‐based deep learning (MoDL) reconstruction network. The registration network receives as input highly undersampled (~22×) respiratory‐resolved images and outputs 3D nonrigid respiratory motion fields between the images. The motion‐informed MoDL performs MoCo reconstruction from undersampled data using the predicted motion fields. The whole deep learning framework, termed as MoCo‐MoDL, was trained end‐to‐end in a supervised manner for simultaneous 3D nonrigid motion estimation and MoCo reconstruction. MoCo‐MoDL was compared with a state‐of‐the‐art nonrigid MoCo CMRA reconstruction technique in 15 retrospectively undersampled datasets and 9 prospectively undersampled acquisitions. Results: The acquisition time for ninefold accelerated CMRA was ~2.5 min. The reconstruction time was ~22 s for the proposed MoCo‐MoDL and ~35 min for the conventional approach. MoCo‐MoDL achieved higher peak SNR (27.86 ± 3.00 vs. 26.71 ± 2.79; P < .05) and structural similarity (0.78 ± 0.06 vs. 0.75 ± 0.06; P < .05) than the conventional approach. Similar vessel length and visual image quality score were obtained with the 2 methods, whereas improved vessel sharpness was observed with MoCo‐MoDL. Conclusion: An end‐to‐end deep learning approach was introduced for simultaneous nonrigid motion estimation and MoCo reconstruction of highly undersampled free‐breathing whole‐heart CMRA. The rapid free‐breathing CMRA acquisition together with the fast reconstruction of the proposed approach promises easy integration into clinical workflow. Abstract : Click here for author‐reader discussions