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Review and Prospect: Artificial Intelligence in Advanced Medical Imaging.
Wang, Shanshan; Cao, Guohua; Wang, Yan; Liao, Shu; Wang, Qian; Shi, Jun; Li, Cheng; Shen, Dinggang.
Afiliação
  • Wang S; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China.
  • Cao G; Pengcheng Laboratrory, Shenzhen, China.
  • Wang Y; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Liao S; School of Computer Science, Sichuan University, Chengdu, China.
  • Wang Q; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
  • Shi J; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Li C; School of Communication and Information Engineering, Shanghai University, Shanghai, China.
  • Shen D; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China.
Front Radiol ; 1: 781868, 2021.
Article em En | MEDLINE | ID: mdl-37492170
ABSTRACT
Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Radiol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Radiol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China