Exact gram filtering and efficient backprojection for iterative CT reconstruction

• Published in: Medical physics (Lancaster) Vol. 49; no. 5; p. 3080
• Main Authors: Shu, Ziyu, Entezari, Alireza
• Format: Journal Article
• Language: English
• Published: United States 01.05.2022
• Subjects: Algorithms; Image Processing, Computer-Assisted - methods; Phantoms, Imaging; Tomography, X-Ray Computed - methods; sinogram interpolation; box splines; gram filter; reconstruction algorithm; 3D X-ray tomography; voxel-basis; detector blur effect
• ISSN: 2473-4209
• DOI: 10.1002/mp.15547

Forward and backprojections are the basis of all model-based iterative reconstruction (MBIR) methods. However, computing these accurately is time-consuming. In this paper, we present a method for MBIR in parallel X-ray beam geometry that utilizes a Gram filter to efficiently implement forward and backprojection. We propose using voxel-basis and modeling its footprint in a box spline framework to calculate the Gram filter exactly and improve the performance of backprojection. In the special case of parallel X-ray beam geometry, the forward and backprojection can be implemented by an estimated Gram filter efficiently if the sinogram signal is bandlimited. In this paper, a specialized sinogram interpolation method is proposed to eliminate the bandlimited prerequisite and thus improve the reconstruction accuracy. We build on this idea by utilizing the continuity of the voxel-basis' footprint, which provides a more accurate sinogram interpolation and further improves the efficiency and quality of backprojection. In addition, the detector blur effect can be efficiently accounted for in our method to better handle realistic scenarios. The proposed method is tested on both phantom and real computed tomography (CT) images under different resolutions, sinogram sampling steps, and noise levels. The proposed method consistently outperforms other state-of-the-art projection models in terms of speed and accuracy for both backprojection and reconstruction. We proposed a iterative reconstruction methodology for 3D parallel-beam X-ray CT reconstruction. Our experimental results demonstrate that the proposed methodology is accurate, fast, and reproducible, and outperforms alternative state-of-the-art projection models on both backprojection and reconstruction results significantly.