Abstract #0014

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

Veronika Spieker^1,2,3, Jonathan Stelter⁴, Wenqi Huang², Hannah Eichhorn^1,2, Kilian Weiss⁵, Rickmer Braren⁴, Veronika A Zimmer², Kerstin Hammernik², Claudia Prieto^3,6,7, Dimitrios C Karampinos⁴, and Julia A Schnabel^1,2,6

¹Institute of Machine Learning in Biomedical Imaging, Helmholtz Center Munich, Munich, Germany, ²School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ³Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile, ⁴School of Medicine and Health, Technical University of Munich, Munich, Germany, ⁵Philips GmbH, Hamburg, Germany, ⁶School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁷School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Synopsis

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

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.

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