Segmentation-free x-ray energy spectrum estimation for computed tomography

TL;DR

This paper introduces a segmentation-free method for estimating X-ray energy spectra in CT using dual-energy material decomposition, improving accuracy in noisy and artifact-laden images for clinical applications.

Contribution

The study develops a novel segmentation-free spectrum estimation technique leveraging dual-energy decomposition, overcoming limitations of previous methods that require segmentation.

Findings

Accurately estimates X-ray spectra in simulated and real phantom data.

Outperforms existing methods in noisy and artifact-affected images.

Potential for improved dose calculation and artifact correction in clinical CT.

Abstract

X-ray energy spectrum plays an essential role in imaging and related tasks. Due to the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and are usually suffered from various limitations. The recently proposed indirect transmission measurement-based method requires at least the segmentation of one material, which is insufficient for CT images of highly noisy and with artifacts. To combat for the bottleneck of spectrum estimation using segmented CT images, in this study, we develop a segmentation-free indirect transmission measurement based energy spectrum estimation method using dual-energy material decomposition. The general principle of the method is to compare polychromatic forward projection with raw projection to calibrate a set of unknown weights which are used to express the unknown spectrum together with a set of model spectra. After applying dual-energy material decomposition using high- and low-energy raw projection data, polychromatic forward projection is conducted on material-specific images. The unknown weights are then iteratively updated to minimize the difference between the raw projection and estimated projection. Both numerical simulations and experimental head phantom are used to evaluate the proposed method. The results indicate that the method provides accurate estimate of the spectrum and it may be attractive for dose calculations, artifacts correction and other clinical applications.