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Abstract #3500

Generative Image Prior Constrained Subspace Reconstruction for High-Resolution MRSI

Ruiyang Zhao1,2, Zepeng Wang1,3, and Fan Lam1,3
1Beckman Institute for Advanced Science and Technology, Urbana, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States, 3Department of Bioengineering, University of illinois Urbana-Champaign, Urbana, IL, United States


We propose a novel method that integrates generative image priors with subspace constrained MRSI reconstruction. A sufficient and flexible image representation was first generated by adapting a pretrained StyleGAN to subject-specific anatomical images. We validate that StyleGAN can be flexibly adapted to accurately represent different contrast of the same subject. The adapted GAN prior is then used to model the spatial coefficients in the subspace-based reconstruction. Improved performance over the original subspace reconstruction in the SPICE framework is demonstrated using simulation and in vivo data.

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