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Abstract #0499

Rapid approximate Bayesian $$$T_2$$$ analysis under Rician noise using deep initialization

Jonathan Doucette1,2, Christian Kames1,2, Christoph Birkl3, and Alexander Rauscher1,2,4,5
1UBC MRI Research Centre, Vancouver, BC, Canada, 2Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada, 3Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria, 4Radiology, University of British Columbia, Vancouver, BC, Canada, 5Pediatrics, University of British Columbia, Vancouver, BC, Canada

Rapid approximate Bayesian parameter inference is investigated for $$$T_2$$$ analysis of multi spin-echo signals. We demonstrate that rapid inference using Rician noise distributions is possible using maximum likelihood estimation (MLE) if the MLE procedure is initialized with samples drawn from an approximate Bayesian posterior. This posterior is learned using a conditional variational autoencoder (CVAE) network, trained on exclusively on simulated data. Nevertheless, we show good generalization to three diverse datasets, including improved inference accuracy compared to standard nonnegative least squares-based methods which implicitly assume Gaussian noise.

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