Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography

TL;DR

This paper introduces a local modeling approach for scatter estimation in polyenergetic X-ray CT, significantly improving descattering accuracy and quantitative reconstruction by fitting models adaptively to similar local data.

Contribution

It presents a novel local fitting method for scatter estimation in X-ray CT, outperforming global models and recent deep learning approaches in accuracy.

Findings

Local models reduce scatter-induced density errors by over 50%.

Adaptive fitting to local neighborhoods improves descattering performance.

Simple local models achieve state-of-the-art results.

Abstract

We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a training set. X-ray CT is widely used in medical and industrial applications. X-ray scatter, if not accounted for during reconstruction, creates a loss of contrast in CT reconstructions and introduces severe artifacts including cupping, shading, and streaks. Even when these qualitative artifacts are not apparent, scatter can pose a major obstacle in obtaining quantitatively accurate reconstructions. Our approach to estimating scatter is, first, to generate a training set of 2D radiographs with and without scatter using particle transport simulation software. To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it. We compared local and global (fit on full data sets) versions of several X-ray scatter models, including two from the recent literature, as well as a recent deep learning-based scatter model, in the context of descattering and quantitative density reconstruction of simulated, spherically symmetrical, single-material objects comprising shells of various densities. Our results show that, when applied locally, even simple models provide state-of-the-art descattering, reducing the error in density reconstruction due to scatter by more than half.