Artificial intelligence in four-dimensional imaging for motion management in radiation therapy

Artificial intelligence in four-dimensional imaging for motion management in radiation therapy

Four-dimensional imaging (4D-imaging) plays a critical role in achieving precise motion management in radiation therapy. However, challenges remain in 4D-imaging such as a long imaging time, suboptimal image quality, and inaccurate motion estimation. With the tremendous success of artificial intelli...

Description complète

Détails bibliographiques
Publié dans:Artificial intelligence review. - 1998. - 58(2025), 4 vom: 09. Apr.
Auteur principal: Yinghui, Wang (Auteur)
Autres auteurs: Haonan, Xiao, Jing, Wang, Lu, Wang, Wen, Li, Zhuoran, Jiang, Ge, Ren, Shaohua, Zhi, Josh, Qian, Jianrong, Dai, Kuo, Men, Lei, Ren, Xiaofeng, Yang, Tian, Li, Jing, Cai
Format: Article en ligne
Langue:English
Publié: 2025
Accès à la collection:Artificial intelligence review
Sujets:Journal Article 4D-imaging Artificial intelligence Radiation therapy motion management
LEADER 01000caa a22002652c 4500
001 NLM392709090
003 DE-627
005 20250926232317.0
007 cr uuu---uuuuu
008 250918s2025 xx |||||o 00| ||eng c
024 7 |a 10.1007/s10462-025-11109-w  |2 doi 
028 5 2 |a pubmed25n1581.xml 
035 |a (DE-627)NLM392709090 
035 |a (NLM)40963558 
035 |a (PII)103 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Yinghui, Wang  |e verfasserin  |4 aut 
245 1 0 |a Artificial intelligence in four-dimensional imaging for motion management in radiation therapy 
264 1 |c 2025 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Revised 26.09.2025 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a Four-dimensional imaging (4D-imaging) plays a critical role in achieving precise motion management in radiation therapy. However, challenges remain in 4D-imaging such as a long imaging time, suboptimal image quality, and inaccurate motion estimation. With the tremendous success of artificial intelligence (AI) in the image domain, particularly deep learning, there is great potential in overcoming these challenges and improving the accuracy and efficiency of 4D-imaging without the need for hardware modifications. In this review, we provide a comprehensive overview of how these AI-based methods could drive the evolution of 4D-imaging for motion management. We discuss the inherent issues associated with multiple 4D modalities and explore the current research progress of AI in 4D-imaging. Furthermore, we delve into the unresolved challenges and limitations in 4D-imaging and provide insights into the future direction of this field 
650 4 |a Journal Article 
650 4 |a 4D-imaging 
650 4 |a Artificial intelligence 
650 4 |a Radiation therapy motion management 
700 1 |a Haonan, Xiao  |e verfasserin  |4 aut 
700 1 |a Jing, Wang  |e verfasserin  |4 aut 
700 1 |a Lu, Wang  |e verfasserin  |4 aut 
700 1 |a Wen, Li  |e verfasserin  |4 aut 
700 1 |a Zhuoran, Jiang  |e verfasserin  |4 aut 
700 1 |a Ge, Ren  |e verfasserin  |4 aut 
700 1 |a Shaohua, Zhi  |e verfasserin  |4 aut 
700 1 |a Josh, Qian  |e verfasserin  |4 aut 
700 1 |a Jianrong, Dai  |e verfasserin  |4 aut 
700 1 |a Kuo, Men  |e verfasserin  |4 aut 
700 1 |a Lei, Ren  |e verfasserin  |4 aut 
700 1 |a Xiaofeng, Yang  |e verfasserin  |4 aut 
700 1 |a Tian, Li  |e verfasserin  |4 aut 
700 1 |a Jing, Cai  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Artificial intelligence review  |d 1998  |g 58(2025), 4 vom: 09. Apr.  |w (DE-627)NLM098184490  |x 0269-2821  |7 nnas 
773 1 8 |g volume:58  |g year:2025  |g number:4  |g day:09  |g month:04 
856 4 0 |u http://dx.doi.org/10.1007/s10462-025-11109-w  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 58  |j 2025  |e 4  |b 09  |c 04