Efficient Image Reconstruction and Practical Decomposition for Dual-energy Computed Tomography

TL;DR

This paper introduces an iterative reconstruction and decomposition method for dual-energy CT that improves image quality and noise suppression, especially under sparse view conditions, outperforming existing techniques.

Contribution

It proposes a novel optimization-based reconstruction algorithm with total variation regularization and an image domain decomposition method using penalized least squares, demonstrating superior performance.

Findings

Enhanced noise suppression and edge preservation in reconstructed images

Superior accuracy in material decomposition compared to state-of-the-art methods

Effective performance on real DECT data from head phantom experiments

Abstract

Dual-energy computed tomography (DECT) has shown great potential and promising applications in advanced imaging fields for its capabilities of material decomposition. However, image reconstructions and decompositions under sparse views dataset suffers severely from multi factors, such as insufficiencies of data, appearances of noise, and inconsistencies of observations. Under sparse views, conventional filtered back-projection type reconstruction methods fails to provide CT images with satisfying quality. Moreover, direct image decomposition is unstable and meet with noise boost even with full views dataset. This paper proposes an iterative image reconstruction algorithm and a practical image domain decomposition method for DECT. On one hand, the reconstruction algorithm is formulated as an optimization problem, which containing total variation regularization term and data fidelity term. The alternating direction method is utilized to design the corresponding algorithm which shows faster convergence speed compared with the existing ones. On the other hand, the image domain decomposition applies the penalized least square (PLS) estimation on decomposing the material mappings. The PLS includes linear combination term and the regularization term which enforces the smoothness on estimation images. The authors implement and evaluate the proposed joint method on real DECT projections and compare the method with typical and state-of-the-art reconstruction and decomposition methods. The experiments on dataset of an anthropomorphic head phantom show that our methods have advantages on noise suppression and edge reservation, without blurring the fine structures in the sinus area in the phantom. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to reconstruction accuracy and decomposition quality.