Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies
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 n...
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doaj-7759f3a137ec4d6aacc83b648063f8132020-11-24T20:49:17ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182015-01-01201510.1155/2015/265497265497Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT StudiesJianhua Wang0Jianrong Dai1Yongjie Jing2Yanan Huo3Tianye Niu4Department of Radiation Oncology, Ningbo Treatment Center, Ningbo Lihuili Hospital, Ningbo, Zhejiang 315000, ChinaDepartment of Radiation Oncology, Cancer Institute (Hospital), Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021, ChinaKey Laboratory of Particle and Radiation Imaging of Chinese Ministry of Education, Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, ChinaSir Run Run Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, ChinaTumor 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.http://dx.doi.org/10.1155/2015/265497 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianhua Wang Jianrong Dai Yongjie Jing Yanan Huo Tianye Niu |
spellingShingle |
Jianhua Wang Jianrong Dai Yongjie Jing Yanan Huo Tianye Niu Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies Computational and Mathematical Methods in Medicine |
author_facet |
Jianhua Wang Jianrong Dai Yongjie Jing Yanan Huo Tianye Niu |
author_sort |
Jianhua Wang |
title |
Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies |
title_short |
Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies |
title_full |
Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies |
title_fullStr |
Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies |
title_full_unstemmed |
Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies |
title_sort |
methodology for registration of shrinkage tumors in head-and-neck ct studies |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2015-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2015/265497 |
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