Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion

TL;DR

This paper introduces a novel polyenergetic convolutional scatter model integrated into CBCT reconstruction, significantly improving quantitative accuracy and artifact reduction by adaptively accounting for scatter during the iterative process.

Contribution

The work presents a new scatter model fusion technique that enhances CBCT reconstruction accuracy and reduces bias, outperforming existing pre-calculated methods.

Findings

Enhanced electron density estimation in CBCT

Significant artifact mitigation compared to fASKS

Adaptive scatter modeling improves quantitative imaging

Abstract

Scatter can account for large errors in cone-beam CT (CBCT) due to its wide field of view, and its complicated nature makes its compensation difficult. Iterative polyenergetic reconstruction algorithms offer the potential to provide quantitative imaging in CT, but they are usually incompatible with scatter contaminated measurements. In this work, we introduce a polyenergetic convolutional scatter model that is directly fused into the reconstruction process, and exploits information readily available at each iteration for a fraction of additional computational cost. We evaluate this method with numerical and real CBCT measurements, and show significantly enhanced electron density estimation and artifact mitigation over pre-calculated fast adaptive scatter kernel superposition (fASKS). We demonstrate our approach has two levels of benefit: reducing the bias introduced by estimating scatter prior to reconstruction; and adapting to the spectral and spatial properties of the specimen.