Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model.
Phys Med Biol
; 64(3): 035018, 2019 01 31.
Article
en En
| MEDLINE
| ID: mdl-30577033
Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as 'MECT-NSS'. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Tomografía Computarizada por Rayos X
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Modelos Estadísticos
Tipo de estudio:
Qualitative_research
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Phys Med Biol
Año:
2019
Tipo del documento:
Article