Dr. Huanmei Wu Profile

Dr. Huanmei Wu

Assistant Professor at Indiana Univ-Purdue Univ at Indianapolis

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Proceedings Article | 18 March 2008 Paper

Correlation identification between internal/external motion signals

Huanmei Wu, Qingya Zhao, Ross Berbeco, Seiko Nishioka, Hiroki Shirato, Steve Jiang

Proceedings Volume 6913, 69131P (2008) https://doi.org/10.1117/12.773995

KEYWORDS: Phase shifts, Tumors, Radiotherapy, Statistical analysis, Cancer, Motion models, Radiation effects, Motion analysis, Imaging systems, Oncology

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Tumor motion induced by patient breathing decreases the effectiveness of radiation treatment. Image guided radiation treatment (IGRT) is an advanced approach for cancer radiation treatment. The success of IGRT is largely dependent on the accurate localization of tumor in real-time. There are two major imaging approaches currently in use to localize a tumor: internal imaging and external imaging. Internal imaging determines the tumor locations by directly x-ray of the tumor area. It is accurate however radiation dose is a big concern. External imaging derives the internal tumor locations through an external mark on the patient surface. It is radiation dose free however the insufficient accuracy limits its wide application. Integrating the internal and external signals together is necessary for reliable radiation treatment and acceptable patient radiation exposure. Our work tries to identify the correlation patterns between internal/external signals and the influential factors so that the hybrid signal will give desire accuracy in dose delivery while limiting radiation exposure to the patients. Both theoretical simulation based on sinusoidal functions and statistical analysis on real patient data are performed. The sinusoidal simulation will identify the potential influence factors of different correlation conditions. The results have demonstrated the various correlation patterns with amplitude various, frequency changes (duration changes), phase shifts, and baseline drift. The results will aid the statistical analytical on real-patients to identify the dominant factors of the internal/external motion signals for a specific patients. The described work is very useful in advanced IGRT to update the internal/external correlation in real-time for better cancer patient care.

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