Reconstruction of Whole-Heart Cardiac Radial MRI using Neural Network Transfer Learning Approach

In a clinical setting, multiple breath-hold, multi-slice, ECG-gated cine MR (CMR) Cartesian acquisition is a gold standard. Multiple breath-holds in standard CMR acquisition can result in slice-misalignment due to inconsistent breath-hold positions and it forces long exam time. To reduce CMR scan time and to avoid slice-misalignment, under-sampled non-Cartesian (NC) trajectories are useful but lead to artifacts. This paper proposes U-Net based transfer-learning approach with NUFFT (NUFFT TL-Net) to reconstruct artifact-free whole heart, radial CMR images. The preliminary experiments show improved performance of the proposed NUFFT TL-Net both visually and in terms of evaluation parameters than contemporary NUFFT U-Net.