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Abstract #0870

Fast MRSI Reconstruction Combining Linear and Nonlinear Manifold Models

Yahang Li1,2, Zepeng Wang 1,2, Aaron Anderson 2,3, Ruiyang Zhao 2,4, Paul Arnold 2,3, Graham Huesmann 2,3,5, and Fan Lam 1,2,4
1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, Urbaba, IL, United States, 3Neuroscience Institute, Carle Foundation Hospital, Urbaba, IL, United States, 4Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States, 5School of Molecular and Cellular Biology, University of Illinois Urbana-Champaign, Urbana, IL, United States

Synopsis

Keywords: Image Reconstruction, SpectroscopyA computationally efficient MRSI reconstruction method is presented. The proposed problem formulation integrates a subspace model of the high-dimensional spatiotemporal function (SPICE) and a network-based learned projector on to a low-dimensional manifold of generic spectroscopic signals. The subspace representation allows for more flexible spatiotemporal sampling designs than using nonlinear manifold constraint alone, while the manifold constraint effectively regularizes the subspace fitting, especially at higher orders. An efficient algorithm is designed to solve the optimization problem. The benefits of the proposed synergy have been demonstrated using simulations as well as experimental 31P and 1H-MRSI data.

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