Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging

TL;DR

This paper introduces a new cone-beam CT reconstruction algorithm that models measurement blur and noise correlations, resulting in improved image accuracy and trabecular bone metrics in high-resolution imaging.

Contribution

The paper presents GPL-BC, a novel penalized-likelihood reconstruction method that incorporates blur and correlated noise models for enhanced high-resolution cone-beam CT imaging.

Findings

GPL-BC achieves lower bias than traditional methods.

GPL-BC provides more accurate trabecular bone segmentation.

GPL-BC yields the most precise trabecular thickness measurement.

Abstract

We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized-Likelihood objective function which incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPL-BC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared to deblurring followed by FDK, a model based method without blur, and a model based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model based methods without blur and/or correlation to a registered {\mu}CT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each reconstruction approach as well as the {\mu}CT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255 mm, as compared to the {\mu}CT derived value of 0.193 mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271 mm, 0.309 mm, and 0.335 mm, respectively).