MRI Reconstruction via Data Driven Markov Chain with Joint Uncertainty Estimation

• URL: http://arxiv.org/abs/2202.01479v1
• Date: Thu, 3 Feb 2022 09:13:49 GMT
• Title: MRI Reconstruction via Data Driven Markov Chain with Joint Uncertainty Estimation
• Authors: Guanxiong Luo, Martin Heide, Martin Uecker
• Abstract summary: We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. The data-driven Markov chains are constructed from the generative model learned from a given image database. The performance of the method is evaluated on an open dataset using 10-fold accelerated acquisition.
• Score: 3.5751623095926806
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstruction techniques, samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for the image, which can be obtained with conventional methods, the minimum mean square error (MMSE) estimate and uncertainty maps can also be computed. The data-driven Markov chains are constructed from the generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement. This provides flexibility because the method can be applied to k-space acquired with different sampling schemes or receive coils using the same pre-trained models. Furthermore, we use a framework based on a reverse diffusion process to be able to utilize advanced generative models. The performance of the method is evaluated on an open dataset using 10-fold accelerated acquisition.

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