On complexity of post-processing in analyzing GATE-driven X-ray spectrum

TL;DR

This paper introduces a novel post-processing method to convert total X-ray spectra into quantized energy intervals, improving image analysis in CT by reducing irregularity and complexity.

Contribution

The study presents a new approach for spectrum segmentation in CT imaging using simulation and complexity analysis, enhancing image quality without dual-energy hardware.

Findings

Irregularity of CT images decreases after applying the method

Complexity measures show reduced entropy and Kolmogorov complexity

Quantitative segmentation criteria improve with the proposed approach

Abstract

Computed Tomography (CT) imaging is one of the most influential diagnostic methods. In clinical reconstruction, an effective energy is used instead of total X-ray spectrum. This approximation causes an accuracy decline. To increase the contrast, single source or dual source dual energy CT can be used to reach optimal values of tissue differentiation. However, these infrastructures are still at the laboratory level, and their safeties for patients are still yet to mature. Therefore, computer modelling of DECT could be used. We propose a novel post-processing approach for converting a total X-ray spectrum into irregular intervals of quantized energy. We simulate a phantom in GATE/GEANT4 and irradiate it based on CT configuration. Inverse Radon transform is applied to the acquired sinogram to construct the Pixel-based Attenuation Matrix (PAM). To construct images represented by each interval, water attenuation coefficient of the interval is extracted from NIST and used in the Hounsfield unit (HU) scale in conjunction with PAM. The CT image is modified by using of an associated normalized photon flux and calculated HU corresponding to the interval. We demonstrate the proposed method efficiency via complexity analysis, using absolute and relative complexities, entropy measures, Kolmogorov complexity, morphological richness, and quantitative segmentation criteria associated with standard fuzzy C-means. The irregularity of the modified CT images decreases over the simulated ones.