PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI

TL;DR

PUERT introduces a probabilistic sampling and explainable reconstruction framework for CS-MRI, jointly optimizing sampling patterns and reconstruction, leading to state-of-the-art results with robust, data-specific sub-sampling and high-quality image recovery.

Contribution

The paper proposes a novel end-to-end probabilistic sampling and reconstruction network that jointly optimizes sampling patterns and reconstruction, incorporating stochastic sampling and a model-based, interpretable network design.

Findings

Achieves state-of-the-art reconstruction quality on MRI datasets.

Generates data-specific sub-sampling patterns for improved performance.

Demonstrates robustness and interpretability in MRI reconstruction.

Abstract

Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i.e., where to sample and how to reconstruct. To deal with both problems simultaneously, we propose a novel end-to-end Probabilistic Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly optimize the sampling pattern and the reconstruction network. Instead of learning a deterministic mask, the proposed sampling subnet explores an optimal probabilistic sub-sampling pattern, which describes independent Bernoulli random variables at each possible sampling point, thus retaining robustness and stochastics for a more reliable CS reconstruction. A dynamic gradient estimation strategy is further introduced to gradually approximate the binarization function in backward propagation, which efficiently preserves the gradient information and further improves the reconstruction quality. Moreover, in our reconstruction subnet, we adopt a model-based network design scheme with high efficiency and interpretability, which is shown to assist in further exploitation for the sampling subnet. Extensive experiments on two widely used MRI datasets demonstrate that our proposed PUERT not only achieves state-of-the-art results in terms of both quantitative metrics and visual quality but also yields a sub-sampling pattern and a reconstruction model that are both customized to training data.