MRI Reconstruction Using Deep Bayesian Estimation

TL;DR

This paper introduces a deep Bayesian inference framework for MRI reconstruction that leverages a generative network as a prior, improving image quality and artifact reduction over existing methods.

Contribution

It develops a novel Bayesian deep learning approach using a generative prior and likelihood-based training, enhancing MRI reconstruction performance.

Findings

Achieved over 5 dB PSNR improvement compared to state-of-the-art methods.

Better preservation of image details and reduction of aliasing artifacts.

Framework generalizes across various MRI reconstruction scenarios.

Abstract

Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction. Methods: We modeled the MRI reconstruction problem with Bayes's theorem, following the recently proposed PixelCNN++ method. The image reconstruction from incomplete k-space measurement was obtained by maximizing the posterior possibility. A generative network was utilized as the image prior, which was computationally tractable, and the k-space data fidelity was enforced by using an equality constraint. The stochastic backpropagation was utilized to calculate the descent gradient in the process of maximum a posterior, and a projected subgradient method was used to impose the equality constraint. In contrast to the other deep learning reconstruction methods, the proposed one used the likelihood of prior as the training loss and the objective function in reconstruction to improve the image quality. Results: The proposed method showed an improved performance in preserving image details and reducing aliasing artifacts, compared with GRAPPA, $\ell_1$-ESPRiT, and MODL, a state-of-the-art deep learning reconstruction method. The proposed method generally achieved more than 5 dB peak signal-to-noise ratio improvement for compressed sensing and parallel imaging reconstructions compared with the other methods. Conclusion: The Bayesian inference significantly improved the reconstruction performance, compared with the conventional $\ell_1$-sparsity prior in compressed sensing reconstruction tasks. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.