Scattering correction based on regularization de-convolution for Cone-Beam CT

TL;DR

This paper introduces a fast, convolution-based deconvolution method with bi-l1-l2 regularization to effectively reduce scatter artifacts and enhance image contrast in Cone-Beam CT imaging.

Contribution

It proposes a novel, regularization-based deconvolution approach for scatter correction in CBCT, improving artifact reduction and image quality.

Findings

Scatter artifacts reduced from 12.930 to 2.133

Method increases image contrast in CBCT

Fast and effective scatter mitigation

Abstract

In Cone-Beam CT (CBCT) imaging systems, the scattering phenomenon has a significant impact on the reconstructed image and is a long-lasting research topic on CBCT. In this paper, we propose a simple, novel and fast approach for mitigating scatter artifacts and increasing the image contrast in CBCT, belonging to the category of convolution-based method in which the projected data is de-convolved with a convolution kernel. A key step in this method is how to determine the convolution kernel. Compared with existing methods, the estimation of convolution kernel is based on bi-l1-l2-norm regularization imposed on both the intermediate the known scatter contaminated projection images and the convolution kernel. Our approach can reduce the scatter artifacts from 12.930 to 2.133.