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Towards intelligent tumor tracking a...

Xu, Qianyi.

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Towards intelligent tumor tracking and setup verification in radiation therapy for lung cancer.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Towards intelligent tumor tracking and setup verification in radiation therapy for lung cancer./
作者:	Xu, Qianyi.
面頁冊數:	172 p.
附註:	Adviser: Robert A. Schowengerdt.
Contained By:	Dissertation Abstracts International68-02B.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3254696

Towards intelligent tumor tracking and setup verification in radiation therapy for lung cancer.
Xu, Qianyi.

Towards intelligent tumor tracking and setup verification in radiation therapy for lung cancer. - 172 p.

Adviser: Robert A. Schowengerdt.

Thesis (Ph.D.)--The University of Arizona, 2007.

Lung cancer is the most deadly cancer in the United States. Radiation therapy uses ionizing radiation with high energy to destroy lung tumor cells by damaging their genetic material, preventing those cells from reproducing. The most challenging aspect of modern radiation therapy for lung cancer is the motion of lung tumors caused by patient breathing during treatment. Most gating based radiotherapy derives the tumor motion from external surrogates and generates a respiratory signal to trigger the beam. We propose a method that monitors internal diaphragm motion, which can provide a respiratory signal that is more highly correlated to lung tumor motion compared to the external surrogates. We also investigate direct tracking of the tumor in fluoroscopic video imagery. We tracked fixed tumor contours in fluoroscopic videos for 5 patients. The predominant tumor displacements are well tracked based on optical flow. Some tumors or nearby anatomy features exhibit severe nonrigid deformation, especially in the supradiaphragmatic region. By combining Active Shape Models and the respiratory signal, the deformed contours are tracked within a range defined in the training period. All the tracking results are validated by a human expert and the proposed methods are promising for applications in radiotherapy. Another important aspect of lung patient treatment is patient setup verification, which is needed to reduce inter- and intra-fractions geometry uncertainties and ensure precise dose delivery. Currently, there is no universally accepted method for lung patient verification. We propose to register 4DCT and 2D x-ray images taken before treatment to derive the couch shifts necessary for precise radiotherapy. The proposed technique leads to improved patient care.Subjects--Topical Terms:

769149
Artificial Intelligence.

Towards intelligent tumor tracking and setup verification in radiation therapy for lung cancer.
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520 $a Lung cancer is the most deadly cancer in the United States. Radiation therapy uses ionizing radiation with high energy to destroy lung tumor cells by damaging their genetic material, preventing those cells from reproducing. The most challenging aspect of modern radiation therapy for lung cancer is the motion of lung tumors caused by patient breathing during treatment. Most gating based radiotherapy derives the tumor motion from external surrogates and generates a respiratory signal to trigger the beam. We propose a method that monitors internal diaphragm motion, which can provide a respiratory signal that is more highly correlated to lung tumor motion compared to the external surrogates. We also investigate direct tracking of the tumor in fluoroscopic video imagery. We tracked fixed tumor contours in fluoroscopic videos for 5 patients. The predominant tumor displacements are well tracked based on optical flow. Some tumors or nearby anatomy features exhibit severe nonrigid deformation, especially in the supradiaphragmatic region. By combining Active Shape Models and the respiratory signal, the deformed contours are tracked within a range defined in the training period. All the tracking results are validated by a human expert and the proposed methods are promising for applications in radiotherapy. Another important aspect of lung patient treatment is patient setup verification, which is needed to reduce inter- and intra-fractions geometry uncertainties and ensure precise dose delivery. Currently, there is no universally accepted method for lung patient verification. We propose to register 4DCT and 2D x-ray images taken before treatment to derive the couch shifts necessary for precise radiotherapy. The proposed technique leads to improved patient care.
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