Are you encountering challenges when using 2D Cardiac MR slices for 3D Cardiac analysis? Here are two major hurdles:
1. Slice thickness (usually 8-10mm) results in missing data between neighboring slices.
2. Misalignment between slices due to motion.
These issues impact the fidelity of 2D CMR stacks as representations of the actual 3D cardiac volume. To address these pain points, our team, led by Zhennong Chen, Quanzheng Li, and Xiang Li, has developed a one-stop solution. Our end-to-end deep learning pipeline, designed to tackle each of these issues, has shown remarkable performance in both simulation studies and real-world CMR datasets. We hope this tool will benefit CMR researchers in their future work!
Access out paper “Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach” for free for 50 days: https://authors.elsevier.com/a/1j0NP3BessvoUL
Many thanks to the co-author: Hui Ren
