An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images

Mohammadreza Negahdar; Albert Zacarias; Rebecca A Milam; Neal Dunlap; Shiao Y. Woo; Amir A. Amini

doi:10.1117/12.912754

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16 April 2012 An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images

Mohammadreza Negahdar, Albert Zacarias, Rebecca A Milam, Neal Dunlap, Shiao Y. Woo, Amir A. Amini

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Proceedings Volume 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging; 83171G (2012) https://doi.org/10.1117/12.912754
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

The treatment plan evaluation for lung cancer patients involves pre-treatment and post-treatment volume CT imaging of the lung. However, treatment of the tumor volume lung results in structural changes to the lung during the course of treatment. In order to register the pre-treatment volume to post-treatment volume, there is a need to find robust and homologous features which are not affected by the radiation treatment along with a smooth deformation field. Since airways are well-distributed in the entire lung, in this paper, we propose use of airway tree bifurcations for registration of the pre-treatment volume to the post-treatment volume. A dedicated and automated algorithm has been developed that finds corresponding airway bifurcations in both images. To derive the 3-D deformation field, a B-spline transformation model guided by mutual information similarity metric was used to guarantee the smoothness of the transformation while combining global information from bifurcation points. Therefore, the approach combines both global statistical intensity information with local image feature information. Since during normal breathing, the lung undergoes large nonlinear deformations, it is expected that the proposed method would also be applicable to large deformation registration between maximum inhale and maximum exhale images in the same subject. The method has been evaluated by registering 3-D CT volumes at maximum exhale data to all the other temporal volumes in the POPI-model data.

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Mohammadreza Negahdar, Albert Zacarias, Rebecca A Milam, Neal Dunlap, Shiao Y. Woo, and Amir A. Amini "An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83171G (16 April 2012); https://doi.org/10.1117/12.912754

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