1Radiology, University of Utah, Salt lake city, UT, United States
Ungated cardiac perfusion MRI can offer a simple and an efficient means of data acquisition especially for patients with arrhythmias. However image quality of the reconstructed images can be affected from cardiac and respiratory motion. Here we propose a new reconstruction framework that can offer improved image quality when there is significant inter-frame motion in the data. By using model-reference images and a bi-directional diffeomorphic registration tool, motion is estimated and suppressed leading to improved signal sparsity in the temporal dimension. Results presented using in-vivo data show the promise of the proposed framework.