DE-NIK: Leveraging Dual-Echo Data for Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit k-Space Representations

Motivation: Neural implicit k-space representations (NIK) enable binning-free respiratory-resolved MR reconstructions in a data-driven manner. The multi-dimensionality of MR, i.e., provided in dual-echo acquisitions, is expected to improve reconstruction performance and allows for further echo-processing. Goal(s): A Dual-Echo-NIK that takes advantage of the redundant data present in two echoes and enables subsequent water-fat-separation. Approach: We propose three Dual-Echo-NIK variants trained (1) individually, (2) jointly and (3) in an echo-modulated way. Motion-resolved echo and water-fat reconstructions are evaluated on a free-breathing phantom simulation and in-vivo. Results: Quantitative simulations demonstrate improved performance for the modulated Dual-Echo-NIK. In-vivo reconstructions reveal sharper reconstructions when both echoes are utilized. Impact: The Dual-Echo Neural Implicit k-space Representations indicate how echo information can lead to improved motion-resolved reconstructions, including subsequent water-fat separations. Echo-modulation can further enhance reconstruction performance and offers the potential to reduce acquisition times for training data.