Deep-learning based motion-compensated A-LIKNet for cardiac Cine MRI reconstruction

Motivation: Cardiac Cine MRI is commonly used for assessing cardiac function. However, extended acquisition times may cause patient discomfort or can result in respiratory motion artifacts and slice misalignments due to multiple breath-holds. Goal(s): We aim to accelerate data acquisition into a single breath-hold ($$$\sim$$$24×) with spatial-temporal sharing along the cardiac cycle for accurate morphological and functional reconstruction. Approach: We integrated inter-frame motion field estimations with a deep learning-based reconstruction. The motion-compensated A-LIKNet was trained on 115 subjects and tested on 14 subjects. Results: The proposed method reconstructs high-quality images, especially improving morphological accuracy, and thus enables cardiac Cine imaging in a single breath-hold. Impact: The proposed deep learning-based motion-compensated A-LIKNet can efficiently reconstruct highly undersampled cardiac Cine MRI for up to 24× accelerated acquisitions of a single breath-hold. Results demonstrate higher morphological authenticity, sharper details, and reduced artifacts compared to other methods.