Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.

Shim, Jae Hun; Choi, Se Young; Chang, In Ho; Park, Sung Bin

Shim, Jae Hun; Choi, Se Young; Chang, In Ho; Park, Sung Bin.

Afiliação

Shim JH; Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Choi SY; Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Chang IH; Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Park SB; Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.

Medicina (Kaunas) ; 59(9)2023 Sep 17.

Article em En | MEDLINE | ID: mdl-37763796

Assuntos

Aprendizado Profundo; Urolitíase; Humanos; Urolitíase/diagnóstico por imagem; Processos Mentais; Tomografia Computadorizada por Raios X

Palavras-chave

deep learning; phantoms, imaging; radiation dosage; tomography, X-ray computed; urolithiasis

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Urolitíase / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Medicina (Kaunas) Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google