A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort. METHODS: The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST). RESULTS: Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant. CONCLUSION: The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.

Free-running 3D whole-heart T1 and T2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction.

PURPOSE: To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial T 1 $$ {T}_1 $$ and T 2 $$ {T}_2 $$ maps and cine images, enabling a ∼ $$ \sim $$ 3-min scan. METHODS: Data were acquired using a free-running 3D golden-angle radial readout interleaved with inversion recovery and T 2 $$ {T}_2 $$ -preparation pulses. After correction for translational respiratory motion, non-rigid cardiac-motion-corrected reconstruction with dictionary-based low-rank compression and patch-based regularization enabled 3D whole-heart T 1 $$ {T}_1 $$ and T 2 $$ {T}_2 $$ mapping at any given cardiac phase as well as whole-heart cardiac cine imaging. The framework was validated and compared with established methods in 11 healthy subjects. RESULTS: Good quality 3D T 1 $$ {T}_1 $$ and T 2 $$ {T}_2 $$ maps and cine images were reconstructed for all subjects. Septal T 1 $$ {T}_1 $$ values using the proposed approach ( 1200 ± 50 $$ 1200\pm 50 $$ ms) were higher than those from a 2D MOLLI sequence ( 1063 ± 33 $$ 1063\pm 33 $$ ms), which is known to underestimate T 1 $$ {T}_1 $$ , while T 2 $$ {T}_2 $$ values from the proposed approach ( 51 ± 4 $$ 51\pm 4 $$ ms) were in good agreement with those from a 2D GraSE sequence ( 51 ± 2 $$ 51\pm 2 $$ ms). CONCLUSION: The proposed technique provides 3D T 1 $$ {T}_1 $$ and T 2 $$ {T}_2 $$ maps and cine images with isotropic spatial resolution in a single ∼ $$ \sim $$ 3.3-min scan.