Abstract #2943

Probabilistic Atlas-Based Image Reconstruction for Ultrafast MR Spectroscopic Imaging

Yudu Li^1,2,3, Yibo Zhao², Wen Jin^2,4, Rong Guo^2,5, Shirui Luo³, Yao Li⁶, Volodymyr Kindratenko^3,4,7, Mark A. Anastasio^1,2,4,8, Brad P. Sutton^1,2,8, and Zhi-Pei Liang^2,4

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, ⁴Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵Siemens Medical Solutions USA, Inc., St. Louis, MO, United States, ⁶School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁷Siebel School of Computing and Data Science, 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, AI/ML Image Reconstruction, MR spectroscopic imaging

Motivation: Ultrafast MRSI significantly accelerates MRSI scans but faces challenges in image reconstruction due to high dimensionality and low SNR.

Goal(s): To improve image reconstruction for ultrafast MRSI.

Approach: A new method is proposed to effectively incorporate spatiospectral priors, combining spectral basis functions for spectral constraints, a generative atlas for spatial priors, and a sparse component for subject-specific image features.

Results: The proposed method has been applied to SPICE. Both simulation and in vivo experiments showed significantly improved accuracy and reduced uncertainty over existing subspace-based and edge-preserving regularization-based reconstruction methods.

Impact: With the capability to reduce imaging dimensionality and improve SNR, the proposed method can further accelerate MRSI scans, making high-speed high-resolution MRSI possible for practical applications.

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