Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis

TL;DR

This paper introduces a deep residual learning method for sparse view CT reconstruction that leverages persistent homology analysis to effectively remove streaking artifacts, achieving high-quality images with faster computation.

Contribution

The paper presents a novel deep residual learning architecture guided by persistent homology analysis to improve sparse view CT reconstruction.

Findings

Significantly improved image quality in sparse view CT.

Several orders of magnitude faster than iterative methods.

Effective artifact removal demonstrated on real patient data.

Abstract

Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive. To address this issue, here we propose a novel deep residual learning approach for sparse view CT reconstruction. Specifically, based on a novel persistent homology analysis showing that the manifold of streaking artifacts is topologically simpler than original ones, a deep residual learning architecture that estimates the streaking artifacts is developed. Once a streaking artifact image is estimated, an artifact-free image can be obtained by subtracting the streaking artifacts from the input image. Using extensive experiments with real patient data set, we confirm that the proposed residual learning provides significantly better image reconstruction performance with several orders of magnitude faster computational speed.