Joint group sparsity-based motion-compensated deep learning reconstruction for 3D whole-heart joint T1/T2 mapping

Motivation: Myocardial T1 and T2 mapping is crucial in the assessment of cardiovascular disease. 3D whole-heart joint T1/T2 mapping approaches have been proposed, however they require long reconstruction times. Goal(s): By leveraging deep learning (DL)-based techniques, we aim to significantly reduce the reconstruction times for 3D whole-heart joint T1/T2 mapping, while maintaining high-quality results. Approach: Recently a joint group sparsity-based DL approach was proposed for image reconstruction of undersampled multi-contrast MRI data. Here, we propose to extend this approach for non-rigid motion-corrected reconstructions for multi-contrast 3D data for joint T1/T2 mapping. Results: Our approach achieves good agreement with reference techniques, while outperforming single-contrast reconstructions. Impact: Joint group sparsity-based deep learning non-rigid motion-corrected reconstruction for multi-dimensional joint 3D T1/T2 whole-heart mapping achieves good agreement with reference techniques and outperforms single-contrast reconstructions. The approach significantly reduces reconstruction times, making it feasible for clinical applications.