Evidence map›Paper›PMID 40727376›Full record

ArticleQuantitative imaging in medicine and surgery2025

Effect of respiration-induced motion on a three-dimensional magnetic resonance imaging-based adaptive radiotherapy workflow in a 1.5T magnetic resonance linear accelerator.

Yongchang Wu, Hang Yu, Qing Xiao, Jing Li, Weige Wei, Lian Duan, Sen Bai, Guangjun Li

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Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yongchang Wu *Department of Radiation Oncology, West China Hospital, Sichuan University, Chengdu, China.
Hang Yu *Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, China.
Qing Xiao *Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, China.
Jing LiDepartment of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, China.
Weige WeiDepartment of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, China.
Lian DuanDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Sen BaiDepartment of Radiation Oncology, West China Hospital, Sichuan University, Chengdu, China.
Guangjun LiDepartment of Radiation Oncology, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The long acquisition time of three-dimensional (3D) magnetic resonance imaging (MRI) makes it vulnerable to motion-induced artifacts such as blurring and ghosting, which may compromise target delineation and dose accuracy. Although motion management strategies such as four-dimensional MRI and cine MRI have been proposed, the specific influence of respiratory parameters-particularly amplitude and frequency-on the geometric and dosimetric precision of magnetic resonance-guided adaptive radiotherapy (RT) remains inadequately quantified. This study thus aimed to systematically evaluate how respiratory-induced linear translational motion affects delineation accuracy and dose distribution in MRI-based adaptive RT. Methods: An MR-compatible motion phantom was employed to replicate patient-specific respiratory-induced translational motion, with amplitude and frequency variations extracted from real patient waveforms being incorporated. Eight distinct respiratory patterns were generated, and MR images were acquired with standard spin-spin relaxation time-weighted (T2W) three-dimensional (3D) Cartesian sequences for each pattern. The internal target volume (ITV) delineated by clinicians based on 3D MR images was compared with the reference ITV (ITV Results: Respiratory amplitude significantly affected delineation accuracy and dosimetric consistency. The DSC decreased linearly with increasing amplitude, from 0.96 at 2.50 mm to 0.83 at 12.50 mm, while the HD and MSD increased proportionally (2.62 to 6.32 mm and 0.08 to 0.64 mm, respectively). Dosimetric analysis showed a notable reduction in ITV Conclusions: This study investigated the impact of respiratory-induced linear translational motion on ITV delineation and dosimetric accuracy in MR-guided RT. It was found that large respiratory amplitudes significantly compromised geometric and dosimetric precision, whereas frequency had minimal influence. The results emphasize the limitations of 3D Cartesian MRI due to motion-averaged artifacts and support the development of advanced imaging techniques for improving ITV delineation accuracy.

Indexed as

adapt-to-shape (ATS)delineation accuracydosimetric outcomesMagnetic resonance-guided radiotherapy (MR-guided RT)respiratory motion

Identifiers

PMID40727376
PMCPMC12290815

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.