Abstract #0168

Multi-Contrast 3D Fast Spin-Echo T2 Shuffling Reconstruction with Score-Based Deep Generative Priors

Sidharth Kumar¹, Asad Aali¹, and Jonathan I Tamir^1,2,3

¹Chandra Family Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States, ²Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States, ³Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionScore-based generative modeling has emerged as a powerful tool for modeling image priors and has recently been used to solve ill-posed inverse problems in various domains including MRI reconstruction. Here we extend the framework to reconstruct multi-contrast 3D fast spin-echo (FSE), i.e. T2 Shuffling data. This is achieved by constraining the posterior sampling reconstruction to a low-dimensional subspace and training a score model on images from this subspace. We demonstrate a proof-of-principal reconstruction of data with no model mismatch, i.e. generated from the forward model.

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