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Proceedings Paper

Evaluation of lung tumor motion management in radiation therapy with dynamic MRI
Author(s): Seyoun Park; Rana Farah; Steven M. Shea; Erik Tryggestad; Russell Hales; Junghoon Lee
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Paper Abstract

Surrogate-based tumor motion estimation and tracing methods are commonly used in radiotherapy despite the lack of continuous real time 3D tumor and surrogate data. In this study, we propose a method to simultaneously track the tumor and external surrogates with dynamic MRI, which allows us to evaluate their reproducible correlation. Four MRIcompatible fiducials are placed on the patient’s chest and upper abdomen, and multi-slice 2D cine MRIs are acquired to capture the lung and whole tumor, followed by two-slice 2D cine MRIs to simultaneously track the tumor and fiducials, all in sagittal orientation. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and group-wise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model to the fiducial segmentations on the 2D cine MRIs. We tested our method on five lung cancer patients. Internal target volume from 4D-CT showed average sensitivity of 86.5% compared to the actual tumor motion for 5 min. 3D tumor motion correlated with the external surrogate signal, but showed a noticeable phase mismatch. The 3D tumor trajectory showed significant cycle-to-cycle variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from fiducials at different locations.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351R (3 March 2017); doi: 10.1117/12.2254676
Show Author Affiliations
Seyoun Park, Johns Hopkins Univ. (United States)
Rana Farah, Johns Hopkins Univ. (United States)
Steven M. Shea, Loyola Univ. (United States)
Erik Tryggestad, Mayo Clinic (United States)
Russell Hales, Johns Hopkins Univ. (United States)
Junghoon Lee, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Baowei Fei, Editor(s)

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