Abstract #0497

Reconstruction of High-Resolution Metabolite Maps from Noisy MRSI Data by Incorporating Spatiospectral Constraints through Learned Kernels

Yudu Li^1,2, Chao Ma^3,4, Shirui Luo², Wen Jin^1,5, Ruihao Liu¹, Georges El Fakhri^3,4, Yao Li⁶, Maria Jaromin², Volodymyr Kindratenko^2,5, Brad Sutton^1,2,7,8, and Zhi-Pei Liang^1,2,5

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, ³Radiology, Harvard Medical School, Boston, MA, United States, ⁴Radiology, Massachusetts General Hospital, Boston, MA, United States, ⁵Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁶School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁷Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁸Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Synopsis

Keywords: Spectroscopy, Image ReconstructionHigh-resolution MR spectroscopic imaging (MRSI) suffers from very low signal-to-noise ratio, which is often addressed using a priori information/constraints. Existing constrained reconstruction methods utilize spectral constraints in the form of spectral subspaces/manifolds, while impose spatial constraints though spatial regularization. This paper presents a novel kernel-based partial separability model for reconstruction of high-resolution of metabolite maps from noisy MRSI data. The proposed model uses spectral basis functions to absorb spectral prior and a learned kernel function to absorb spatial prior. Experimental results demonstrated very encouraging reconstruction performance.

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