Using data-driven Markov chains for MRI reconstruction with Joint Uncertainty Estimation
Guanxiong Luo1, Martin Heide1, and Martin Uecker1,2,3,4
1University Medical Center Göttingen, Göttingen, Germany, 2Institute of Medical Engineering, Graz, Austria, 3German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany, 4Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells'' (MBExC), University of Göttingen, Göttingen, Germany
This works explores the use of data-driven Markov chains that are constructed from generative models for Bayesian MRI reconstruction, where the generative models utilize prior knowledge learned from an existing image database. Given the measured k-space, samples are then drawn from the posterior using Markov chain Monte Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for the image which is obtained with conventional methods, also a minimum mean square error (MMSE) estimate and uncertainty maps can be computed from these samples.
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