Abstract #0675

Accelerated T2 Mapping with GS-Assisted Deep Translation of T1W Image Prior

Ruihao Liu^1,2, Yudu Li^2,3, Rong Guo^2,4, Yibo Zhao^2,5, Ziyu Meng¹, Huixiang Zhuang¹, Tianyao Wang⁶, Yao Li¹, Yiping P. Du¹, and Zhi-Pei Liang^2,5

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, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Siemens Medical Solutions USA, Inc., Urbana, IL, United States, ⁵Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁶Radiology Department, The Fifth People's Hospital of Shanghai, Shanghai, China

Synopsis

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence, T2 mappingDeep learning (DL)-based methods have shown great potential for accelerating T2W imaging by using image prior generated from a companion T1W image. However, quantitative T2 mapping requires multiple T2W images acquired with multi-TE, creating practical problem for the use of DL for accelerated T2 mapping due to insufficient multi-TE training data. This work addresses this problem by using a generalized series model to map a T2W DL prior for one TE to multiple TEs, enabling effective use of T1W image prior for high-quality T2 mapping from sparsely sampled data. The method has been validated using experimental data, producing impressive results.

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