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Abstract #2793

Data-Driven Image Contrast Synthesis from Efficient Mixed-Contrast Sequences

Jonathan I Tamir1, Valentina Taviani2, Shreyas S Vasanawala3, and Michael Lustig1

1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2MR Applications and Workflow, GE Healthcare, Menlo Park, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

Synthetic MR is an attractive paradigm for generating diagnostic MR images with retrospectively chosen scan parameters. Typically, synthetic MR images are produced by collecting measurements at multiple measurement times and fitting to a physical model. Here we propose a two-step approach to contrast synthesis. First, we solve a regularized linear inverse problem to reconstruct images at multiple measurement times. Second, we classify spatio-temporal signals and apply different linear combinations based on the classification. We demonstrate the approach on retrospectively under-sampled T1 Shuffling data, in which 3D FSE is collected at relatively short repetition times (TR), and combined to synthesize image contrast with a long TR. The data-driven approach may be useful for synthesizing MR contrasts from acquisitors with varying measurement parameters.

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