Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study.

Watanabe, Shota; Sakaguchi, Kenta; Kitaguchi, Shigetoshi; Ishii, Kazunari

Watanabe, Shota; Sakaguchi, Kenta; Kitaguchi, Shigetoshi; Ishii, Kazunari.

Afiliación

Watanabe S; Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka 589-8511, Japan; Radiology Center, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka 589-8511, Japan. Electronic address: shouta-w@me
Sakaguchi K; Radiology Center, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka 589-8511, Japan. Electronic address: sakaguchi_kenta@nike.eonet.ne.jp.
Kitaguchi S; Radiology Center, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka 589-8511, Japan. Electronic address: s-kitaguchi@med.kindai.ac.jp.
Ishii K; Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osakasayama, Osaka 589-8511, Japan. Electronic address: ishii@med.kindai.ac.jp.

Phys Med ; 104: 1-9, 2022 Dec.

Article en En | MEDLINE | ID: mdl-36347080

Asunto(s)

Aprendizaje Profundo; Tomografía Computarizada por Rayos X; Tomografía

Palabras clave

Computed tomography; Deep learning-based reconstruction; Low radiation dose; Noise stationarity; Pulmonary nodule volumetry

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Phys Med Asunto de la revista: BIOFISICA / BIOLOGIA / MEDICINA Año: 2022 Tipo del documento: Article

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