Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications.

Nizam, Navid Ibtehaj; Ochoa, Marien; Smith, Jason T; Gao, Shan; Intes, Xavier

Nizam, Navid Ibtehaj; Ochoa, Marien; Smith, Jason T; Gao, Shan; Intes, Xavier.

Afiliación

Nizam NI; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
Ochoa M; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
Smith JT; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
Gao S; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
Intes X; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.

J Biomed Opt ; 27(8)2022 04.

Article en En | MEDLINE | ID: mdl-35484688

Asunto(s)

Aprendizaje Profundo; Tomografía Óptica; Método de Montecarlo; Tomografía Óptica/métodos

Palabras clave

Monte Carlo modeling; biophotonics; deep learning; diffuse optical tomography; diffuse optical topography; fluorescence molecular tomography

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Tomografía Óptica / Aprendizaje Profundo Tipo de estudio: Health_economic_evaluation Idioma: En Revista: J Biomed Opt Asunto de la revista: ENGENHARIA BIOMEDICA / OFTALMOLOGIA Año: 2022 Tipo del documento: Article

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