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Abstract #1106

Accelerated T1 and T*2 Mapping with Scan Specific Unsupervised Networks and Subspace Modeling

Amir Heydari1, Tae Hyung Kim2, Yuting Chen3,4,5, Abbas Ahmadi1, and Berkin Bilgic4,5
1Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran (Islamic Republic of), 2Department of Computer Engineering, Hongik University, Seoul, Korea, Republic of, 3State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 5Radiology, Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Quantitative Imaging, AI/ML Image Reconstruction

Motivation: To provide high fidelity in multi-parametric quantitative MRI reconstruction by integrating subspace modeling with phase priors.

Goal(s): We propose Sub-MAPLE, designed as a self-supervised, model-based approach for multi-parametric mapping, capable of simultaneously estimating T1, T*2, frequency, and proton density maps.

Approach: The proposed framework replaces the signal model by subspace modeling integrated with phase priors, enhancing reconstruction performance and leading to improved multi-parametric mapping.

Results: The proposed method demonstrates superior performance in both multi-contrast reconstruction and multi-parametric mapping compared to state-of-the-art self-supervised AI-based and conventional parallel imaging techniques.

Impact: Sub-MAPLE estimates T1, T*2, frequency, and proton density at high acceleration rates, outperforming the state-of-the-art Joint MAPLE and conventional methods. It incorporates subspace modeling with phase priors, enabling high accuracy mapping from 15-fold accelerated acquisitions.

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Keywords