AIR: fused Analytical and Iterative Reconstruction method for computed tomography

Abstract

Purpose: CT image reconstruction techniques have two major categories: analytical reconstruction (AR) method and iterative reconstruction (IR) method. AR reconstructs images through analytical formulas, such as filtered backprojection (FBP) in 2D and Feldkamp-Davis-Kress (FDK) method in 3D, which can be either mathematically exact or approximate. On the other hand, IR is often based on the discrete forward model of X-ray transform and formulated as a minimization problem with some appropriate image regularization method, so that the reconstructed image corresponds to the minimizer of the optimization problem. This work is to investigate the fused analytical and iterative reconstruction (AIR) method. Methods: Based on IR with L1-type image regularization, AIR is formulated with a AR-specific preconditioner in the data fidelity term, which results in the minimal change of the solution algorithm that replaces the adjoint X-ray transform by the filtered X-ray transform. As a proof-of-concept 2D example of AIR, FBP is incorporated into tensor framelet (TF) regularization based IR, and the formulated AIR minimization problem is then solved through split Bregman method with GPU-accelerated X-ray transform and filtered adjoint X-ray transform. Conclusion: AIR, the fused Analytical and Iterative Reconstruction method, is proposed with a proof-of-concept 2D example to synergize FBP and TF-regularized IR, with improved image resolution and contrast for experimental data. The potential impact of AIR is that it offers a general framework to develop various AR enhanced IR methods, when neither AR nor IR alone is sufficient.