Optimization-Based Image Reconstruction Regularized with Inter-Spectral Structural Similarity for Limited-Angle Dual-Energy Cone-Beam CT

TL;DR

This paper introduces a novel regularization technique based on inter-spectral structural similarity for improved image reconstruction in limited-angle dual-energy cone-beam CT, eliminating the need for spectral measurement or paired training data.

Contribution

The study develops a practical iterative reconstruction method that reduces artifacts in limited-angle dual-energy CBCT without requiring spectral measurements or paired datasets.

Findings

Effective artifact reduction in limited-angle scenarios

Accurate dual-energy image reconstruction without spectral data

Validated with physical and digital phantoms

Abstract

Background: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from LA projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training. Purpose: This work aims to facilitate the clinical applications of fast and low-dose DECBCT by developing a practical solution for image reconstruction in LA-DECBCT. Methods: An inter-spectral structural similarity-based regularization was integrated into the iterative image reconstruction in LA-DECBCT. By enforcing the similarity between the DE images, LA artifacts were efficiently reduced in the reconstructed DECBCT images. The proposed method was evaluated using four physical phantoms and three digital phantoms, demonstrating its efficacy in quantitative DECBCT imaging. Conclusions: The proposed method achieves accurate image reconstruction without the need for X-ray spectra measurement for optimization or paired datasets for model training, showing great practical value in clinical implementations of LA-DECBCT.