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

Yinghui WANG; Haonan XIAO; Jing WANG; Lu WANG; Li WEN; Zhuoran JIANG; Ge REN; Shaohua ZHI; Josh QIAN; Jianrong DAI; Kuo MEN; Lei REN; Xiaofeng YANG; Tian LI; Jing CAI

doi:10.1007/s10462-025-11109-w

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

Yinghui WANG, Haonan XIAO, Jing WANG, Lu WANG, Li WEN, Zhuoran JIANG, Ge REN, Shaohua ZHI, Josh QIAN, Jianrong DAI, Kuo MEN, Lei REN, Xiaofeng YANG, Tian LI^*, Jing CAI^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Article number	103
Number of pages	39
Journal	Artificial Intelligence Review
Volume	58
Issue number	4
DOIs	https://doi.org/10.1007/s10462-025-11109-w
Publication status	Published - Apr 2025
Externally published	Yes

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

Funding

This research was partly supported by research grants of Project of Strategic Importance Fund (P0035421), Projects of RISA (P0043001) and Projects of RI-IWEAR (P0038684) from The Hong Kong Polytechnic University, General Research Fund (15103520, 15104323, 15104822), Innovation and Technology Support Programme (ITS/049/22FP), Health and Medical Research Fund (07183266, 09200576, 10211606), the Health Bureau, The Government of the Hong Kong Special Administrative Region, and the National Natural Science Foundation of China Young Scientist Fund (82202941).

Keywords

4D-imaging
Artificial intelligence
Radiation therapy motion management

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10.1007/s10462-025-11109-wLicence: CC BY-NC-ND

Cite this

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title = "Artificial intelligence in four-dimensional imaging for motion management in radiation therapy",

abstract = "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.",

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author = "Yinghui WANG and Haonan XIAO and Jing WANG and Lu WANG and Li WEN and Zhuoran JIANG and Ge REN and Shaohua ZHI and Josh QIAN and Jianrong DAI and Kuo MEN and Lei REN and Xiaofeng YANG and Tian LI and Jing CAI",

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doi = "10.1007/s10462-025-11109-w",

language = "English",

volume = "58",

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T1 - Artificial intelligence in four-dimensional imaging for motion management in radiation therapy

AU - WANG, Yinghui

AU - XIAO, Haonan

AU - WANG, Jing

AU - WANG, Lu

AU - WEN, Li

AU - JIANG, Zhuoran

AU - REN, Ge

AU - ZHI, Shaohua

AU - QIAN, Josh

AU - DAI, Jianrong

AU - MEN, Kuo

AU - REN, Lei

AU - YANG, Xiaofeng

AU - LI, Tian

AU - CAI, Jing

PY - 2025/4

Y1 - 2025/4

N2 - 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.

AB - 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.

KW - 4D-imaging

KW - Artificial intelligence

KW - Radiation therapy motion management

UR - https://www.scopus.com/pages/publications/85217276466

U2 - 10.1007/s10462-025-11109-w

DO - 10.1007/s10462-025-11109-w

M3 - Journal Article (refereed)

AN - SCOPUS:85217276466

SN - 0269-2821

VL - 58

JO - Artificial Intelligence Review

JF - Artificial Intelligence Review

IS - 4

M1 - 103

ER -

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

Abstract

Bibliographical note

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Keywords

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