On Scale Space Radon Transform, Properties and Application in CT Image Reconstruction

TL;DR

This paper introduces the Scale Space Radon Transform (SSRT) for CT image reconstruction, demonstrating its advantages over traditional Radon Transform methods in reducing artifacts, noise sensitivity, and improving image quality especially with fewer projections.

Contribution

The paper models the X-ray beam using SSRT, incorporating physical system assumptions for better image reconstruction, and adapts the FBP algorithm with SSRT for improved results.

Findings

SSRT-based reconstruction outperforms RT in low-projection scenarios.

SSRT-FBP is more robust to Poisson-Gaussian noise.

SSRT and RT-FBP have similar runtime, with SSRT-FBP showing better noise resilience.

Abstract

Since the Radon transform (RT) consists in a line integral function, some modeling assumptions are made on Computed Tomography (CT) system, making image reconstruction analytical methods, such as Filtered Backprojection (FBP), sensitive to artifacts and noise. In the other hand, recently, a new integral transform, called Scale Space Radon Transform (SSRT), is introduced where, RT is a particular case. Thanks to its interesting properties, such as good scale space behavior, the SSRT has known number of new applications. In this paper, with the aim to improve the reconstructed image quality for these methods, we propose to model the X-ray beam with the Scale Space Radon Transform (SSRT) where, the assumptions done on the physical dimensions of the CT system elements reflect better the reality. After depicting the basic properties and the inversion of SSRT, the FBP algorithm is used to reconstruct the image from the SSRT sinogram where the RT spectrum used in FBP is replaced by SSRT and the Gaussian kernel, expressed in their frequency domain. PSNR and SSIM, as quality measures, are used to compare RT and SSRT-based image reconstruction on Shepp-Logan head and anthropomorphic abdominal phantoms. The first findings show that the SSRT-based method outperforms the methods based on RT, especially, when the number of projections is reduced, making it more appropriate for applications requiring low-dose radiation, such as medical X-ray CT. While SSRT-FBP and RT-FBP have utmost the same runtime, the experiments show that SSRT-FBP is more robust to Poisson-Gaussian noise corrupting CT data.