Markerless tracking of tumor and tissues: A motion model approach

Cheung, Ling Fung; Fujitaka, Shinichirou; Fujii, Takaaki; Miyamoto, Naoki; Takao, Seishin

Markerless tracking of tumor and tissues: A motion model approach

Cheung, Ling Fung; Fujitaka, Shinichirou; Fujii, Takaaki et al.

Medical physics, 2025, 52(2), 1193-1206

Permalink : https://hdl.handle.net/2115/96194

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/96194
タイトル	Markerless tracking of tumor and tissues: A motion model approach
著者	Cheung, Ling Fung ; Fujitaka, Shinichirou ; Fujii, Takaaki ; Miyamoto, Naoki ; Takao, Seishin
著者名 Cheung, Ling Fung
著者名 Fujitaka, Shinichirou
著者名 Fujii, Takaaki
e-Rad_Researcher 00552879 著者名 Miyamoto, Naoki
e-Rad_Researcher 10614216 著者名 Takao, Seishin
言語	英語
キーワード	markerless
	motion model
	tracking
発行日	2025-02
出版者	John Wiley & Sons
収録物名	Medical physics
巻(号)	52(2)
ページ	1193-1206
抄録	Background Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking.
	Purpose We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images.
	Methods A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition.
	Results In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3 mm to less than 1 mm after correction. For the shallow breathing motion trace with a 1.7 mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5 mm to less than 1 mm after correction.
	Conclusion The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	This is the peer reviewed version of the following article: Cheung LF, Fujitaka S, Fujii T, Miyamoto N, Takao S. Markerless tracking of tumor and tissues: A motion model approach. Med Phys. 2025; 52: 1193–1206, which has been published in final form at https://doi.org/10.1002/mp.17524. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
PISSN	0094-2405
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1002/mp.17524