Abstract #1142
T1 and T2 Mapping Using Highly Sparse Unsuppressed Water Signals from MRSI Scans with Generalized Series-Assisted Low-Rank Tensor Modelling
Yudu Li1,2, Rong Guo1,3, Yibo Zhao1,4, Wen Jin1,4, Chao Ma5,6, Shirui Luo2, Georges El Fakhri5,6, Yao Li7, Maria Jaromin2, Volodymyr Kindratenko2,4, Brad Sutton1,2,8,9, and Zhi-Pei Liang1,2,4
1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Siemens Medical Solutions, Urbana, IL, United States, 4Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 5Radiology, Harvard Medical School, Boston, MA, United States, 6Radiology, Massachusetts General Hospital, Boston, MA, United States, 7School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 8Department of Bioengineering, 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 ImagingMR spectroscopic imaging (MRSI) without water suppression provides a unique opportunity to use the unsuppressed water spectroscopic signals for T1 and T2 mapping. This work presents a new image reconstruction method for reconstructing the T1/T2 maps from the highly sparse MRSI data. This method uses a novel generalized series-assisted low-rank tensor model to absorb the high-quality reference MRSI images to constrain the spatial-spectral-parametric variations. Experimental results demonstrated very encouraging reconstruction performance.
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