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Abstract #0811

Highly Accelerated T1 and T2 Mapping in Water-Unsuppressed MRSI Using Generalized Series Modeling and Deep Learning

Yudu Li1,2,3, Rong Guo2,4, Yibo Zhao2, Wen Jin2,5, Shirui Luo3, Kaili Lu6, Li Cao6, Yao Li7, Volodymyr Kindratenko3,5,8, Mark A. Anastasio1,2,5,9, Brad P. Sutton1,2,9, and Zhi-Pei Liang2,5
1Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 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., St. Louis, MO, United States, 5Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 6Department of Neurology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, 7School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 8Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 9Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging, Multiparametric imaging

Motivation: MRSI and quantitative parametric mapping provide complementary tissue information, but separate acquisitions result in long scan times.

Goal(s): To achieve 1-mm whole-brain T1/T2 mapping in water-unsuppressed MRSI for rapid simultaneous metabolic and parametric mapping.

Approach: In data acquisition, we further accelerated the SPICE sequence for T1/T2 mapping using shorter TR, extended readout, and sparser sampling. In image reconstruction, we proposed a novel model-based method to incorporate spatiotemporal priors from metabolic imaging and training data for T1/T2 reconstruction from highly sparse data.

Results: Phantom and in vivo experiments showed the proposed method was accurate and reproducible.

Impact: The proposed method provides a powerful multimodal imaging capability that provides tissue structural and biochemical biomarkers at the same time. This new imaging technology may enable better tissue characterization desired for various research and clinical applications.

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Abstract Presentation Video

Keywords