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Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.
Feng, Jinchao; Sun, Qiuwan; Li, Zhe; Sun, Zhonghua; Jia, Kebin.
Afiliación
  • Feng J; Beijing Univ. of Technology, China.
  • Sun Q; Beijing Lab. of Advanced Information Networks, China.
  • Li Z; Beijing Univ. of Technology, China.
  • Sun Z; Beijing Univ. of Technology, China.
  • Jia K; Beijing Lab. of Advanced Information Networks, China.
J Biomed Opt ; 24(5): 1-12, 2018 12.
Article en En | MEDLINE | ID: mdl-30569669
Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Imagen Asistido por Computador / Redes Neurales de la Computación / Tomografía Óptica Tipo de estudio: Prognostic_studies Idioma: En Revista: J Biomed Opt Asunto de la revista: ENGENHARIA BIOMEDICA / OFTALMOLOGIA Año: 2018 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Imagen Asistido por Computador / Redes Neurales de la Computación / Tomografía Óptica Tipo de estudio: Prognostic_studies Idioma: En Revista: J Biomed Opt Asunto de la revista: ENGENHARIA BIOMEDICA / OFTALMOLOGIA Año: 2018 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos