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Abstract #0062

Neural-network-based accelerated liver water T1 mapping

Elizabeth Huaroc Moquillaza1, Veronika Spieker2,3, Chinmay Rao4, Jakob Meineke5, Kilian Weiss6, Julia A. Schnabel2,3,7, Mariya Doneva5, and Dimitrios C. Karampinos1
1Institute of Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Department Clinical Medicine, Technical University of Munich, Munich, Germany, 2Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany, 3School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 4Leiden University Medical Center, Leiden, Netherlands, 5Philips Innovative Technologien, Hamburg, Germany, 6Philips GmbH Market DACH, Hamburg, Germany, 7School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

Synopsis

Keywords: Liver, Quantitative Imaging

Motivation: Reliable wT1 mapping continuous inversion-recovery Look-Locker (CIR-LL) requires a certain recovery duration, which results in extended acquisition times per slice and limits the liver coverage within one breath-hold.

Goal(s): To reduce the required recovery duration for reliable liver wT1 mapping.

Approach: We propose to combine CIR-LL with a neural network, which is trained to conduct wT1 mapping based on the SVD of a reduced recovery duration.

Results: The proposed time-efficient wT1 liver mapping method provides consistent wT1 values in phantom and in vivo measurements in both volunteers and patients, while preserving lesion detection.

Impact: A neural network-based liver wT1 mapping technique that shortens acquisition times using CIR-LL without sacrificing the quality of quantitative reconstruction results, and holds promise for further mapping applications.

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