The following publications are possibly variants of this publication:
- Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep LearningQingjie Meng, Wenjia Bai, Tianrui Liu, Declan P. O'Regan, Daniel Rueckert. miccai 2022: 248-258 [doi]
- A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and AnalysisInês Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz, Devran Ugurlu, Ihsane Olakorede, Ilkay Öksüz, Bram Ruijsink, Miguel Castelo-Branco, Alistair A. Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King. tbe, 71(3):855-865, March 2024. [doi]
- Learning-Based and Unrolled Motion-Compensated Reconstruction for Cardiac MR CINE ImagingJiaZhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik. miccai 2022: 686-696 [doi]
- Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRIJiaZhen Pan, Manal Hamdi, Wenqi Huang, Kerstin Hammernik, Thomas Küstner, Daniel Rueckert. mia, 91:103017, January 2024. [doi]
- The Challenge of Fetal Cardiac MRI Reconstruction Using Deep LearningDenis Prokopenko, Kerstin Hammernik, Thomas A. Roberts, David F. A. Lloyd, Daniel Rueckert, Joseph V. Hajnal. miccai 2023: 64-74 [doi]
- Super-resolution reconstruction of cardiac MRI using coupled dictionary learningKanwal K. Bhatia, Anthony N. Price, Wenzhe Shi, Joseph V. Hajnal, Daniel Rueckert. isbi 2014: 947-950 [doi]