Volume registration based on 3-D phase correlation for tumor motion estimation in 4-D CT

Xiaoyong Zhang, Noriyasu Homma, Makoto Abe, Norihiro Sugita, Yoshihiro Takai, Yuichiro Narita, Makoto Yoshizawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a three-dimensional (3-D) volume registration method that uses 3-D phase correlation to estimate the respiration-induced tumor motion in four-dimensional (4-D) thorax computed tomography (CT) for radiation therapy. The proposed method is an extension of 2-D phase correlation method to 3-D volume registration. Given two CT volumes obtained from different respiration stages, the tumor motion is modeled as a translational shift between the volumes. The 3-D phase correlation is obtained from the 3-D inverse Fourier transform of a normalized cross power spectrum of the volumes. The tumor motion along three directions is estimated by locating the highest peak in the 3-D phase correlation. In order to improve the estimation accuracy, we extend the 3-D phase correlation to sub-voxel accuracy. Experimental results demonstrate the effectiveness of the proposed method relative to a conventional 2-D phase correlation-based method.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages5095-5098
Number of pages4
DOIs
Publication statusPublished - 2013 Oct 31
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 2013 Jul 32013 Jul 7

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period13/7/313/7/7

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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