Spectral CT Reconstruction via Low-rank Representation and Structure Preserving Regularization

TL;DR

This paper introduces a spectral CT reconstruction method that leverages low-rank representation and structure-preserving regularization to improve image quality and material decomposition, outperforming existing techniques.

Contribution

It proposes a novel spectral CT reconstruction algorithm combining low-rank correlation and structure regularization with an adaptive parameter strategy, enhancing accuracy over prior methods.

Findings

Achieves higher reconstruction accuracy than SART, TVM, LRTV, and SSCMF.

Improves feature similarity (FSIM) by 40.4% on average in simulations.

Demonstrates effectiveness on real mouse spectral CT data.

Abstract

With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cutting into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel, which cause the image qualities to be severely degraded by noise and artifacts. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper. To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel. Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively.