A novel method for reconstructing CT images in GATE/GEANT4 with application in medical imaging: a complexity analysis approach
For reconstructing CT images in the clinical setting, "effective energy" is usually used instead of the total X-ray spectrum. This approximation causes an accuracy decline. We proposed to quantize the total X-ray spectrum into irregular intervals to preserve accuracy. A phantom consisting...
| Autores: | , , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2020 |
| País: | España |
| Recursos: | Universitat Oberta de Catalunya (UOC) |
| Repositório: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/125327 |
| Acesso em linha: | https://hdl.handle.net/10609/125327 |
| Access Level: | Acceso aberto |
| Palavra-chave: | complexity CT image FCM GATE/Geant4 Hounsfield unit pixel-based attenuation matrix complejidad imagen del TAC unidad Hounsfield matriz de atenuación basada en píxeles complexitat imatge del TAC unitat Hounsfield matriu d'atenuació basada en píxels Bioinformatics Bioinformàtica Bioinformática |
| Resumo: | For reconstructing CT images in the clinical setting, "effective energy" is usually used instead of the total X-ray spectrum. This approximation causes an accuracy decline. We proposed to quantize the total X-ray spectrum into irregular intervals to preserve accuracy. A phantom consisting of the skull, rib bone, and lung tissues was irradiated with CT configuration in GATE/GEANT4. We applied inverse Radon transform to the obtained Sinogram to construct a Pixel-based Attenuation Matrix (PAM). PAM was then used to weight the calculated Hounsfield unit scale (HU) of each interval's representative energy. Finally, we multiplied the associated normalized photon flux of each interval to the calculated HUs. The performance of the proposed method was evaluated in the course of Complexity and Visual analysis. Entropy measurements, Kolmogorov complexity, and morphological richness were calculated to evaluate the complexity. Quantitative visual criteria (i.e., PSNR, FSIM, SSIM, and MSE) were reported to show the effectiveness of the fuzzy C-means approach in the segmenting task. |
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