Super‐MoCo‐MoDL: A combined super-resolution and motion-corrected undersampled deep-learning reconstruction framework for three-dimensional whole-heart cardiac magnetic resonance imaging

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BACKGROUND: Cardiac magnetic resonance (CMR) is a well-established imaging modality for the assessment of cardiovascular diseases. However, attainable image resolution remains lower than that of X-ray computed tomography (CT) due to long scan times and the need for respiratory motion correction. In this work, we combine a previously proposed motion-corrected model-based deep-learning reconstruction for undersampled three-dimensional (3D) whole-heart CMR with data-consistent super-resolution to enable high-resolution 3D whole-heart CMR from significantly shortened scans. METHODS: Our proposed framework, Super motion-corrected (MoCo) model-based deep-learning (MoDL), utilizes two neural networks; the first estimates non-rigid respiratory motion from zero-padded and zero-filled bin images, the second applies these fields in an iterative motion-corrected model-based alternating direction method of multipliers (alternating direction method of multipliers) reconstruction which alternates between applying a super-resolving U-Net and imposing data-consistency in the acquired center of k-space. The framework was trained using 156 isotropic-resolution free-breathing 3D datasets. It was subsequently applied to prospective anisotropic low-resolution free-breathing 3D data acquired in a cohort of congenital heart disease (CHD) patients, and to prospective undersampled and low-resolution data acquired in a cohort of patients with suspected coronary artery disease (CAD). RESULTS: Isotropic resolution whole-heart 3D images were reconstructed from ∼ 0.8- and ∼ 2.1-minute scans, for CHD patients at 1.5-mm resolution and suspected-CAD patients at 0.9-mm resolution, respectively, representing an overall scan acceleration of ∼ 18-fold in each case. Visual inspection, expert image quality scores and rankings, and quantitative vessel sharpness measurements demonstrated that the Super-MoCo-MoDL reconstructions produced sharp high-quality images that were comparable with high-resolution acquisitions. For patients with suspected CAD, comparison was made with computed tomography coronary angiography (CTCA), demonstrating that coronary plaque visualization was possible with the Super-MoCo-MoDL technique. CONCLUSION: Super-MoCo-MoDL is able to reconstruct high-resolution 3D whole-heart images from low-resolution and undersampled anisotropic acquisitions.

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