Reconstructing T2 Maps of the Brain from Highly Sparse k-Space Data with Generalized Series-Assisted Deep Learning
Ruihao Liu1, Yudu Li2,3, Ziyu Meng1, Yue Guan1, Ziwen Ke1, Tianyao Wang4, Yao Li1, Yiping P. Du1, and Zhi-Pei Liang2,3
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Beckman Institute for 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, 4Radiology Department, The Fifth People's Hospital of Shanghai, Shanghai, China
Quantitative T2 maps of brain tissues are useful for diagnosis and characterization of a number of diseases, including neurodegenerative disorders. This work presents a new learning-based method for the reconstruction of T2 maps from highly sparse k-space data acquired in accelerated T2-mapping experiments. The proposed method synergistically integrates generalized series modeling with deep learning, which effectively captures the underlying signal structures of T2-weighted images with variable TEs and a priori information from prior scans (e.g., data from the Human Connectome Project). The proposed method has been validated using experimental data, producing improved brain T2 maps over the state-of-the-art methods.
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