A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.

Chang, Yushi; Jiang, Zhuoran; Segars, William Paul; Zhang, Zeyu; Lafata, Kyle; Cai, Jing; Yin, Fang-Fang; Ren, Lei

Chang, Yushi; Jiang, Zhuoran; Segars, William Paul; Zhang, Zeyu; Lafata, Kyle; Cai, Jing; Yin, Fang-Fang; Ren, Lei.

Afiliación

Chang Y; Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.
Jiang Z; Medical Physics Graduate Program, Duke University Durham, NC, United States of America.
Segars WP; Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.
Zhang Z; Medical Physics Graduate Program, Duke University Durham, NC, United States of America.
Lafata K; Department of Radiology, Duke University Medical Center, Durham, NC, United States of America.
Cai J; Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC, United States of America.
Yin FF; Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.
Ren L; Medical Physics Graduate Program, Duke University Durham, NC, United States of America.

Phys Med Biol ; 66(11)2021 05 31.

Article en En | MEDLINE | ID: mdl-34061044

Asunto(s)

Tomografía Computarizada Cuatridimensional; Respiración; Humanos; Movimiento (Física); Fantasmas de Imagen; Torso

Palabras clave

XCAT phantom; deformation vector field; generative adversarial network; virtual trial

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Respiración / Tomografía Computarizada Cuatridimensional Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Phys Med Biol Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

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