Fused Analytical and Iterative Reconstruction (AIR) via modified proximal forward-backward splitting: a FDK-based iterative image reconstruction example for CBCT

TL;DR

This paper introduces a novel analytical iterative reconstruction (AIR) framework that combines analytical and iterative CT reconstruction methods using a modified proximal forward-backward splitting algorithm, leading to faster convergence and improved image quality in CBCT.

Contribution

The paper develops a general AIR framework integrating analytical reconstruction into iterative methods with a modified PFBS algorithm, demonstrating improved convergence and image quality in CBCT.

Findings

AIR achieves faster convergence due to eigenvalues close to unity.

AIR improves image contrast and resolution in CBCT.

AIR reduces artifacts compared to traditional AR and IR methods.

Abstract

This work is to develop a general framework, namely analytical iterative reconstruction (AIR) method, to incorporate analytical reconstruction (AR) method into iterative reconstruction (IR) method, for enhanced CT image quality and reconstruction efficiency. Specifically, AIR is established based on the modified proximal forward-backward splitting (PFBS) algorithm, and its connection to the filtered data fidelity with sparsity regularization is discussed. As a result, AIR decouples data fidelity and image regularization with a two-step iterative scheme, during which an AR-projection step updates the filtered data fidelity term, while a denoising solver updates the sparsity regularization term. During the AR-projection step, the image is projected to the data domain to form the data residual, and then reconstructed by certain AR to a residual image which is then weighted together with previous image iterate to form next image iterate. Intuitively since the eigenvalues of AR-projection operator are close to the unity, PFBS based AIR has a fast convergence. Such an advantage is rigorously established through convergence analysis and numerical computation of convergence rate. The proposed AIR method is validated in the setting of circular cone-beam CT with AR being FDK and total-variation sparsity regularization, and has improved image quality from both AR and IR. For example, AIR has improved visual assessment and quantitative measurement in terms of both contrast and resolution, and reduced axial and half-fan artifacts.