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Registration of lung nodules using a semi-rigid model: method and preliminary results.

Publication ,  Journal Article
Sun, S; Rubin, GD; Paik, D; Steiner, RM; Zhuge, F; Napel, S
Published in: Med Phys
February 2007

The tracking of lung nodules across computed tomography (CT) scans acquired at different times for the same patient is helpful for the determination of malignancy. We are developing a nodule registration system to facilitate this process. We propose to use a semi-rigid method that considers principal structures surrounding the nodule and allows relative movements among the structures. The proposed similarity metric, which evaluates both the image correlation and the degree of elastic deformation amongst the structures, is maximized by a two-layered optimization method, employing a simulated annealing framework. We tested our method by simulating five cases that represent physiological deformation as well as different nodule shape/size changes with time. Each case is made up of a source and target scan, where the source scan consists of a nodule-free patient CT volume into which we inserted ten simulated lung nodules, and the target scan is the result of applying a known, physiologically based nonrigid transformation to the nodule-free source scan, into which we inserted modified versions of the corresponding nodules at the same, known locations. Five different modification strategies were used, one for each of the five cases: (1) nodules maintain size and shape, (2) nodules disappear, (3) nodules shrink uniformly by a factor of 2, (4) nodules grow uniformly by a factor of 2, and (5) nodules grow nonuniformly. We also matched 97 real nodules in pairs of scans (acquired at different times) from 12 patients and compared our registration to a radiologist's visual determination. In the simulation experiments, the mean absolute registration errors were 1.0+/-0.8 mm (s.d.), 1.1+/-0.7 mm (s.d.), 1.0+/-0.7 mm (s.d.), 1.0+/-0.6 mm (s.d.), and 1.1+/- 0.9 mm (s.d.) for the five cases, respectively. For the 97 nodule pairs in 12 patient scans, the mean absolute registration error was 1.4+/-0.8 mm (s.d.).

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Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

February 2007

Volume

34

Issue

2

Start / End Page

613 / 626

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Subtraction Technique
  • Solitary Pulmonary Nodule
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Models, Biological
 

Citation

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MLA
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Sun, S., Rubin, G. D., Paik, D., Steiner, R. M., Zhuge, F., & Napel, S. (2007). Registration of lung nodules using a semi-rigid model: method and preliminary results. Med Phys, 34(2), 613–626. https://doi.org/10.1118/1.2432073
Sun, Shaohua, Geoffrey D. Rubin, David Paik, Robert M. Steiner, Feng Zhuge, and Sandy Napel. “Registration of lung nodules using a semi-rigid model: method and preliminary results.Med Phys 34, no. 2 (February 2007): 613–26. https://doi.org/10.1118/1.2432073.
Sun S, Rubin GD, Paik D, Steiner RM, Zhuge F, Napel S. Registration of lung nodules using a semi-rigid model: method and preliminary results. Med Phys. 2007 Feb;34(2):613–26.
Sun, Shaohua, et al. “Registration of lung nodules using a semi-rigid model: method and preliminary results.Med Phys, vol. 34, no. 2, Feb. 2007, pp. 613–26. Pubmed, doi:10.1118/1.2432073.
Sun S, Rubin GD, Paik D, Steiner RM, Zhuge F, Napel S. Registration of lung nodules using a semi-rigid model: method and preliminary results. Med Phys. 2007 Feb;34(2):613–626.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

February 2007

Volume

34

Issue

2

Start / End Page

613 / 626

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Subtraction Technique
  • Solitary Pulmonary Nodule
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Models, Biological