Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging-Reference-Cited by-同舟云学术

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Published:2023 Issue:4 Volume:22 Page:401-414
ISSN:1347-3182
Container-title:Magnetic Resonance in Medical Sciences
language:en
Short-container-title:magn reson med sci

Author:

Fujima Noriyuki¹,Kamagata Koji²,Ueda Daiju³,Fujita Shohei⁴,Fushimi Yasutaka⁵,Yanagawa Masahiro⁶,Ito Rintaro⁷,Tsuboyama Takahiro⁶,Kawamura Mariko⁷,Nakaura Takeshi⁸,Yamada Akira⁹,Nozaki Taiki¹⁰,Fujioka Tomoyuki¹¹,Matsui Yusuke¹²,Hirata Kenji¹³,Tatsugami Fuminari¹⁴,Naganawa Shinji⁷

Affiliation:

1. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital

2. Department of Radiology, Juntendo University Graduate School of Medicine

3. Department of Diagnostic and Interventional Radiology, Graduate School of Medicine

4. Department of Radiology, University of Tokyo

5. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine

6. Department of Radiology, Osaka University Graduate School of Medicine

7. Department of Radiology, Nagoya University Graduate School of Medicine

8. Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine

9. Department of Radiology, Shinshu University School of Medicine

10. Department of Radiology, Keio University School of Medicine

11. Department of Diagnostic Radiology, Tokyo Medical and Dental University

12. Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University

13. Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University

14. Department of Diagnostic Radiology, Hiroshima University

Publisher

Japanese Society for Magnetic Resonance in Medicine

Subject

Radiology, Nuclear Medicine and imaging

Link

https://www.jstage.jst.go.jp/article/mrms/22/4/22_rev.2023-0047/_pdf

Reference123 articles.

1. 1. Tshering Vogel DW, Thoeny HC. Cross-sectional imaging in cancers of the head and neck: how we review and report. Cancer Imaging 2016; 16:20.

2. 2. Bhatnagar P, Subesinghe M, Patel C, Prestwich R, Scarsbrook AF. Functional imaging for radiation treatment planning, response assessment, and adaptive therapy in head and neck cancer. Radiographics 2013; 33:1909–1929.

3. 3. King AD, Thoeny HC. Functional MRI for the prediction of treatment response in head and neck squamous cell carcinoma: potential and limitations. Cancer Imaging 2016; 16:23.

4. 4. Albano D, Bruno F, Agostini A, et al. Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging. Jpn J Radiol 2022; 40:341–366.

5. 5. Touska P, Connor SEJ. Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications. Br J Radiol 2019; 92:20190513.

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