Program Information
Deformable Registration Method by Joint Using TPS and B-Spline for Lung Cancer in Radiotherapy
N Han1,2*, Y Xie1, K Fang2, (1) Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, (2) College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China
SU-E-J-78 Sunday 3:00PM - 6:00PM Room: Exhibit HallPurpose: To increase the accuracy of deformable registration method for lung deformation problem caused by respiratory in CT image.
Methods:One issue in radiotherapy is the lung deformation during respiratory motion. Respiratory motion can lead to the changes in sizes, shape, and location of tumors, which can't be solved by rigid registration. It becomes a vital factor for prognosis when Stereotactic Body Radiation Therapy (SBRT) is employed to treat lung cancer, because of the high dose rate and shorten treatment time. Interpolation is an essential step in deformable registration. Thin plate spline (TPS) and B-spline are two widely used interpolation methods in radiotherapy. TPS can achieve high speed calculation, but may result in large errors because of its global effect on the image and B-spline takes overlong run-time caused by the iterative calculation in the optimization process. In this work, we proposed a novel method to combine these two methods. First, TPS is used to register two CT images rapidly. Then B-spline is used to refine the registration result.
Results:In our experiment, a couple of clinical lung images are simulated using the proposed method. The result indicates that both of run-time and registration quality have been obviously improved, compared with the conventional methods solely using either TPS or B-spline. As for lung cancer in radiotherapy, when high accuracy is achieved, the results show large differences between these two methods in iteration times and computation time. The registration method using B-spline takes 3624.5s with 66 iteration times. While the proposed method can do less work (1999.4s with 51 iteration times) to achieve the same accuracy. However, if only lower accuracy is required, the differences are not so apparent.
Conclusion:The proposed method takes advantage of both of these two methods and can perform good adaptivity and robustness in registration of lung CT images.
Funding Support, Disclosures, and Conflict of Interest: This work is supported in part by grants from National Natural Science Foundation of China (NSFC: 81171402), NSFC Joint Research Fund for Overseas Research Chinese, Hong Kong and Macao Young Scholars (30928030), National Basic Research Program 973 (2010CB732606) from Ministry of Science and Technology of China, Guangdong Innovative Research Team Program (No. 2011S013) of China, Science and Technological Program for Dongguan's Higher Education, Science and Research, and Health Care Institutions(Grant No.2011108101001), and grant Comprehensive Strategic Cooperation Project of Guangdong province and Chinese academy of sciences (Grant No.2011B090300079).
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