Abstract #0100

Accelerating Free-Breathing Liver MRI at 7T using Recurrent Inference Machines

Mitra Tavakkoli^1,2, Daisy van den Berg³, Bobby A. Runderkamp⁴, Wietske van der Zwaag^5,6, Michael D. Noseworthy^1,2,7, and Matthan W.A. Caan³

¹Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, ²Imaging Research Centre, St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada, ³Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, ⁴Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, ⁵Spinoza Center for Neuroimaging, Royal Netherlands Academy for Arts and Sciences, Amsterdam, Netherlands, ⁶Netherlands Institute for Neuroscience, KNAW, Computational Cognitive Neuroscience and Neuroimaging, Amsterdam, Netherlands, ⁷Department of Radiology, McMaster University, Hamilton, ON, Canada

Synopsis

Keywords: Liver, High-Field MRI

Motivation: Liver imaging could benefit from high SNR and spatial resolution offered at 7T but suffers from B₁+ inhomogeneities and lengthy acquisitions during breathing.

Goal(s): To improve the reconstruction accuracy for prospectively undersampled free-breathing liver data while reducing processing time.

Approach: Free-breathing, rf-shimmed imaging reconstructed with CIRIM for rapid image reconstruction at high undersampling factors.

Results: CIRIM reconstructed breath-hold liver data for acceleration factors up to 6 without significant loss of detail and was successfully applied to prospectively undersampled data acquired at isotropic resolutions of 1.5mm and 1.35mm.

Impact: This work advances ultra-high field, free-breathing liver MRI with deep-learning reconstruction, offering improved motion robustness over CS. It paves the way for prospectively undersampled, submillimeter resolution free-breathing acquisitions in future studies while maintaining short scan durations and minimizing patient discomfort.

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