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Abstract #1617

Joint MR T1 and T2* Parameter Mapping with Scan Specific Unsupervised Networks

Amir Heydari1, Tae Hyung Kim2,3,4, Abbas Ahmadi1, and Berkin Bilgic3,4
1Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran (Islamic Republic of), 2Department of Computer Engineering, Hongik University, Seoul, Korea, Republic of, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Radiology, Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging, Image ReconstructionWe introduce a new method for joint T1 and T2* MR parameter mapping in MAPLE framework which takes advantage of multi-echo multi-flip angle (MEMFA) gradient echo data. It combines joint reconstruction of MEMFA data with a joint relaxation signal model to improve parameter estimation. The proposed method estimates T1 and T2* parameters jointly with enough flexibility to incorporate different standard and state-of-the-art reconstruction methods, including zero-shot self-supervised learning (ZS-SSL) reconstruction. It improves the reconstruction performance of the methods with learnable parameters by offering a physics-based additional regularization into their optimization process and exploiting spatio-temporal correlations.

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Keywords