Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

被引:0
|
作者
Hossein Arabi
Habib Zaidi
机构
[1] Geneva University Hospital,Division of Nuclear Medicine and Molecular Imaging
[2] Geneva University,Geneva University Neurocenter
[3] University Medical Center Groningen,Department of Nuclear Medicine and Molecular Imaging, University of Groningen
[4] University of Southern Denmark,Department of Nuclear Medicine
关键词
Molecular imaging; Radiation therapy; Artificial intelligence; Deep learning; Quantitative imaging;
D O I
暂无
中图分类号
学科分类号
摘要
This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting.
引用
收藏
相关论文
共 50 条
  • [1] Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy
    Arabi, Hossein
    Zaidi, Habib
    [J]. EUROPEAN JOURNAL OF HYBRID IMAGING, 2020, 4 (01):
  • [2] Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications
    D'Angelo, Tommaso
    Caudo, Danilo
    Blandino, Alfredo
    Albrecht, Moritz H.
    Vogl, Thomas J.
    Gruenewald, Leon D.
    Gaeta, Michele
    Yel, Ibrahim
    Koch, Vitali
    Martin, Simon S.
    Lenga, Lukas
    Muscogiuri, Giuseppe
    Sironi, Sandro
    Mazziotti, Silvio
    Booz, Christian
    [J]. JOURNAL OF CLINICAL ULTRASOUND, 2022, 50 (09) : 1414 - 1431
  • [3] Artificial Intelligence and Deep Learning in Sensors and Applications
    Yuan, Shyan-Ming
    Hong, Zeng-Wei
    Cheng, Wai-Khuen
    [J]. SENSORS, 2024, 24 (10)
  • [4] Artificial intelligence and deep learning for biomedical applications
    Khanna, Pritee
    Tanveer, Mohammad
    Prasad, Mukesh
    Lin, Chin-Teng
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (10) : 13137 - 13137
  • [5] Artificial intelligence and deep learning for biomedical applications
    [J]. Multimedia Tools and Applications, 2022, 81 : 13137 - 13137
  • [6] Applications of Artificial Intelligence and Deep Learning in Glaucoma
    Chen, Dinah
    Ran, Emma Anran
    Tan, Ting Fang
    Ramachandran, Rithambara
    Li, Fei
    Cheung, Carol
    Yousefi, Siamak
    Tham, Clement C. Y.
    Ting, Daniel S. W.
    Zhang, Xiulan
    Al-Aswad, Lama A.
    [J]. ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY, 2023, 12 (01): : 80 - 93
  • [7] Clinical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in the Imaging of Gliomas: A Systematic Review
    Alhasan, Ayman S.
    [J]. CUREUS JOURNAL OF MEDICAL SCIENCE, 2021, 13 (11)
  • [8] Advances in Artificial Intelligence, Machine Learning and Deep Learning Applications
    Haleem, Muhammad Salman
    [J]. ELECTRONICS, 2023, 12 (18)
  • [9] Deep learning and artificial intelligence in dental diagnostic imaging
    Katsumata, Akitoshi
    [J]. JAPANESE DENTAL SCIENCE REVIEW, 2023, 59 : 329 - 333
  • [10] Applications of deep learning method of artificial intelligence in education
    Zhang, Fan
    Wang, Xiangyu
    Zhang, Xinhong
    [J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2024,