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Abstract #1370

MR fingerprinting with implicit neural representation (FINR) for free-breathing 3D whole-liver water T1, water T2, fat fraction, and R2* mapping

Chao Li1,2, Jiahao Li1,3, Jinwei Zhang1,3, Eddy Solomon1, Alexey Dimov1, Pascal Spincemaille1, Thanh Nguyen1, Martin Prince1, and Yi Wang1
1Radiology, Weill Cornell Medicine, New York, NY, United States, 2Applied and Engineering Physics, Cornell Univeristy, Ithaca, NY, United States, 3Meinig School of Biomedical Engineering, Cornell Univeristy, Ithaca, NY, United States

Synopsis

Keywords: MR Fingerprinting, Liver

Motivation: Water T1, water T2, R2* and PDFF are quantitative measures that have shown utility in the diagnosis of various liver diseases but currently require separate acquisitions, typically using many breath-holds.

Goal(s): To develop a single MR acquisition for free-breathing 3D whole-liver quantification of water T1, water T2, PDFF, R2*.

Approach: We propose a neural network using implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images.

Results: Our results showed small bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values compared to conventional breath-holding scans.

Impact: Our work enables 3D whole-liver quantification of water T1, water T2, PDFF, and R2* in a single free-breathing MR acquisition. It also provides a novel solution to reconstruct 5D MRI images (3D spatial + contrast + motion dimension) using INR.

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Keywords