Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density

TL;DR

This paper introduces U-RDBFB, a deep unfolding neural network that combines physics-based modeling and learned parameters for improved ROI CT image reconstruction from limited data, outperforming existing methods.

Contribution

The paper proposes a novel deep unfolding network based on the DBFB algorithm for ROI CT reconstruction with limited data, integrating physics and learning for enhanced performance.

Findings

U-RDBFB outperforms state-of-the-art methods in ROI CT reconstruction.

Effective handling of few-view truncated data with non-convex fidelity and sparsity.

Supervised learning of key parameters improves reconstruction quality.

Abstract

This paper presents a new method for reconstructing regions of interest (ROI) from a limited number of computed tomography (CT) measurements. Classical model-based iterative reconstruction methods lead to images with predictable features. Still, they often suffer from tedious parameterization and slow convergence. On the contrary, deep learning methods are fast, and they can reach high reconstruction quality by leveraging information from large datasets, but they lack interpretability. At the crossroads of both methods, deep unfolding networks have been recently proposed. Their design includes the physics of the imaging system and the steps of an iterative optimization algorithm. Motivated by the success of these networks for various applications, we introduce an unfolding neural network called U-RDBFB designed for ROI CT reconstruction from limited data. Few-view truncated data are effectively handled thanks to a robust non-convex data fidelity term combined with a sparsity-inducing regularization function. We unfold the Dual Block coordinate Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner. Our experiments show an improvement over several state-of-the-art methods, including a model-based iterative scheme, a multi-scale deep learning architecture, and other deep unfolding methods.