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Abstract #0889

Accelerated T2 Mapping by Integrating Two-Stage Learning with Sparse Modeling

Ziyu Meng1,2, Yudu Li2,3, Rong Guo2,3, Yibo Zhao2,3, Tianyao Wang4, Fanyang Yu2,5, Brad Sutton2,5, Yao Li1, and Zhi-Pei Liang2,3
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Department of Radiology, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China, 5Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

We propose a new method to learn the multi-TE image priors for accelerated T2 mapping. The proposed method has the following key features: a) fully leveraging the Human Connectome Project (HCP) database to learn T2-weighted image priors for a single TE, b) transferring the learned single-TE T2-weighted image priors to multi-TE via deep histogram mapping, c) reducing the learning complexity using a tissue-based training strategy, and d) recovering subject-dependent novel features using sparse modeling. The proposed method has been validated using experimental data, producing very encouraging results.

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