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Abstract #0247

Learning compact latent representations of signal evolution for improved shuffling reconstruction

Yamin Arefeen1, Junshen Xu1, Molin Zhang1, Jacob White1, Berkin Bilgic2,3, and Elfar Adalsteinsson1,4,5
1Massachusetts Institute of Technology, Cambridge, MA, United States, 2Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 3Department of Radiology, Harvard Medical School, Boston, MA, United States, 4Harvard-MIT Health Sciences and Technology, Cambridge, MA, United States, 5Institute for Medical Engineering and Science, Cambridge, MA, United States


Applying linear subspace constraints in the shuffling forward model enables reconstruction of signal dynamics through reduced degrees of freedom. Other techniques train auto-encoders to learn latent representations of signal evolution to apply as regularization. This work inserts the decoder portion of an auto-encoder directly into the shuffling forward model to reduce degrees of freedom in comparison to linear techniques. We show that auto-encoders represent fast-spin-echo signal evolution with 1 latent variable, in comparison to 3-4 linear coefficients. Then, the reduced degrees of freedom enabled by the decoder improves reconstruction results in comparison to linear constraints in simulation and in-vivo experiments.

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