Unsupervised reconstruction of undersampled 3D whole-heart Cartesian MRimaging using neural fields

Motivation: 3D MRI is fundamental for the assessment of cardiovascular disease but suffers from long scan times. Undersampled reconstruction techniques have been proposed to accelerate the acquisition, but require long computational times for training. Goal(s): To develop an unsupervised undersampled reconstruction approach based on implicit neural-field representations for 3D Cartesian MRI. Approach: Dataset was acquired using image-based-navigator (iNAV). iNAV-based translational motion was corrected in k-space. Undersampled reconstruction was performed using a Neural-Fields. The method is evaluated on undersampled multi-coil data in comparison to a state-of-the-art. Results: The feasible reconstruction results show similar image quality to the state-of-the-art reference, holding promise for future clinical evaluation. Impact: The method proposed can be generalized to any context of reconstruction. The use in digital devices is feasible, ensuring its possible medical use. Furthermore, this work methodology could allow the use of the net architecture given for other research contexts.