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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 265497, 9 pages
Research Article

Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies

1Department of Radiation Oncology, Ningbo Treatment Center, Ningbo Lihuili Hospital, Ningbo, Zhejiang 315000, China
2Department of Radiation Oncology, Cancer Institute (Hospital), Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, China
3Key Laboratory of Particle and Radiation Imaging of Chinese Ministry of Education, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
4The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
5Sir Run Run Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China

Received 31 October 2014; Revised 15 January 2015; Accepted 28 January 2015

Academic Editor: Panagiotis Mavroidis

Copyright © 2015 Jianhua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Tumor shrinkage occurs in many patients undergoing radiotherapy for head-and-neck (H&N) cancer. However, one-to-one correspondence is not always available between voxels of two image sets. This makes intensity-based deformable registration difficult and inaccurate. In this paper, we describe a novel method to increase the performance of the registration in presence of tumor shrinkage. The method combines an image modification procedure and a fast symmetric Demons algorithm to register CT images acquired at planning and posttreatment fractions. The image modification procedure modifies the image intensities of the primary tumor by calculating tumor cell survival rate using the linear quadratic (LQ) model according to the dose delivered to the tumor. A scale operation is used to deal with uncertainties in biological parameters. The method was tested in 10 patients with nasopharyngeal cancer (NPC). Registration accuracy was improved compared with that achieved using the symmetric Demons algorithm. The average Dice similarity coefficient (DSC) increased by 21%. This novel method is suitable for H&N adaptive radiation therapy.