Compressible image registration for thoracic computed tomography images

Edward Castillo, Richard Castillo, Yin Zhang, Thomas Guerrero

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Four dimensional computed tomography (4D CT) image sets contain both tissue motion as well as respiratory induced changes in the CT image characteristics resulting from ventilation. Deformable image registration (DIR) provides a link between the component phase images for extraction of the motion and physiological information. Most current algorithms, such as optical flow, assume incompressibility in their formulation, which is a potential source of error for lung tissue. In this study, we derive two new DIR methods. First, the combined compressible local global (CCLG) formulation accounts for: (1) the compressible nature of the lungs, (2) noise in the images, and (3) the high computational workload required. In order to account for lung compressibility, voxel displacement is modeled by the conservation of mass equation, rather than by the constant voxel intensity assumption employed by optical flow. The effect of noise is reduced by applying a local-global approach to the conservation of mass setting. Finally, the resulting large scale linear systems are solved using a parallelizable, preconditioned conjugate gradient algorithm. The local compressible interpolation (LCI) method is a less computationally intensive variant of the full CCLG method for use in cases where restrictions on computational resources prevent the application of the full CCLG method. The average spatial accuracy of the methods applied to three thoracic CT image sets was determined using large samples of expert-determined landmarks, and found to be 1.59 mm and 1.86 mm for the CCLG and LCI methods, respectively.

Original languageEnglish (US)
Pages (from-to)222-233
Number of pages12
JournalJournal of Medical and Biological Engineering
Volume29
Issue number5
StatePublished - 2009
Externally publishedYes

Fingerprint

Optical flows
Image registration
Tomography
Conservation
Interpolation
Thorax
Tissue
Four-Dimensional Computed Tomography
Compressibility
Ventilation
Linear systems
Lung
Noise
Workload
Research Design

Keywords

  • Compressible flow (COF)
  • Computed tomography (CT)
  • Deformable image registration (DIR)
  • Optical flow (OF)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Medicine(all)

Cite this

Castillo, E., Castillo, R., Zhang, Y., & Guerrero, T. (2009). Compressible image registration for thoracic computed tomography images. Journal of Medical and Biological Engineering, 29(5), 222-233.

Compressible image registration for thoracic computed tomography images. / Castillo, Edward; Castillo, Richard; Zhang, Yin; Guerrero, Thomas.

In: Journal of Medical and Biological Engineering, Vol. 29, No. 5, 2009, p. 222-233.

Research output: Contribution to journalArticle

Castillo, E, Castillo, R, Zhang, Y & Guerrero, T 2009, 'Compressible image registration for thoracic computed tomography images', Journal of Medical and Biological Engineering, vol. 29, no. 5, pp. 222-233.
Castillo, Edward ; Castillo, Richard ; Zhang, Yin ; Guerrero, Thomas. / Compressible image registration for thoracic computed tomography images. In: Journal of Medical and Biological Engineering. 2009 ; Vol. 29, No. 5. pp. 222-233.
@article{fd767f1818e1422bb0c50d095a7a1b32,
title = "Compressible image registration for thoracic computed tomography images",
abstract = "Four dimensional computed tomography (4D CT) image sets contain both tissue motion as well as respiratory induced changes in the CT image characteristics resulting from ventilation. Deformable image registration (DIR) provides a link between the component phase images for extraction of the motion and physiological information. Most current algorithms, such as optical flow, assume incompressibility in their formulation, which is a potential source of error for lung tissue. In this study, we derive two new DIR methods. First, the combined compressible local global (CCLG) formulation accounts for: (1) the compressible nature of the lungs, (2) noise in the images, and (3) the high computational workload required. In order to account for lung compressibility, voxel displacement is modeled by the conservation of mass equation, rather than by the constant voxel intensity assumption employed by optical flow. The effect of noise is reduced by applying a local-global approach to the conservation of mass setting. Finally, the resulting large scale linear systems are solved using a parallelizable, preconditioned conjugate gradient algorithm. The local compressible interpolation (LCI) method is a less computationally intensive variant of the full CCLG method for use in cases where restrictions on computational resources prevent the application of the full CCLG method. The average spatial accuracy of the methods applied to three thoracic CT image sets was determined using large samples of expert-determined landmarks, and found to be 1.59 mm and 1.86 mm for the CCLG and LCI methods, respectively.",
keywords = "Compressible flow (COF), Computed tomography (CT), Deformable image registration (DIR), Optical flow (OF)",
author = "Edward Castillo and Richard Castillo and Yin Zhang and Thomas Guerrero",
year = "2009",
language = "English (US)",
volume = "29",
pages = "222--233",
journal = "Journal of Medical and Biological Engineering",
issn = "1609-0985",
publisher = "Biomedical Engineering Society",
number = "5",

}

TY - JOUR

T1 - Compressible image registration for thoracic computed tomography images

AU - Castillo, Edward

AU - Castillo, Richard

AU - Zhang, Yin

AU - Guerrero, Thomas

PY - 2009

Y1 - 2009

N2 - Four dimensional computed tomography (4D CT) image sets contain both tissue motion as well as respiratory induced changes in the CT image characteristics resulting from ventilation. Deformable image registration (DIR) provides a link between the component phase images for extraction of the motion and physiological information. Most current algorithms, such as optical flow, assume incompressibility in their formulation, which is a potential source of error for lung tissue. In this study, we derive two new DIR methods. First, the combined compressible local global (CCLG) formulation accounts for: (1) the compressible nature of the lungs, (2) noise in the images, and (3) the high computational workload required. In order to account for lung compressibility, voxel displacement is modeled by the conservation of mass equation, rather than by the constant voxel intensity assumption employed by optical flow. The effect of noise is reduced by applying a local-global approach to the conservation of mass setting. Finally, the resulting large scale linear systems are solved using a parallelizable, preconditioned conjugate gradient algorithm. The local compressible interpolation (LCI) method is a less computationally intensive variant of the full CCLG method for use in cases where restrictions on computational resources prevent the application of the full CCLG method. The average spatial accuracy of the methods applied to three thoracic CT image sets was determined using large samples of expert-determined landmarks, and found to be 1.59 mm and 1.86 mm for the CCLG and LCI methods, respectively.

AB - Four dimensional computed tomography (4D CT) image sets contain both tissue motion as well as respiratory induced changes in the CT image characteristics resulting from ventilation. Deformable image registration (DIR) provides a link between the component phase images for extraction of the motion and physiological information. Most current algorithms, such as optical flow, assume incompressibility in their formulation, which is a potential source of error for lung tissue. In this study, we derive two new DIR methods. First, the combined compressible local global (CCLG) formulation accounts for: (1) the compressible nature of the lungs, (2) noise in the images, and (3) the high computational workload required. In order to account for lung compressibility, voxel displacement is modeled by the conservation of mass equation, rather than by the constant voxel intensity assumption employed by optical flow. The effect of noise is reduced by applying a local-global approach to the conservation of mass setting. Finally, the resulting large scale linear systems are solved using a parallelizable, preconditioned conjugate gradient algorithm. The local compressible interpolation (LCI) method is a less computationally intensive variant of the full CCLG method for use in cases where restrictions on computational resources prevent the application of the full CCLG method. The average spatial accuracy of the methods applied to three thoracic CT image sets was determined using large samples of expert-determined landmarks, and found to be 1.59 mm and 1.86 mm for the CCLG and LCI methods, respectively.

KW - Compressible flow (COF)

KW - Computed tomography (CT)

KW - Deformable image registration (DIR)

KW - Optical flow (OF)

UR - http://www.scopus.com/inward/record.url?scp=72049106621&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=72049106621&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:72049106621

VL - 29

SP - 222

EP - 233

JO - Journal of Medical and Biological Engineering

JF - Journal of Medical and Biological Engineering

SN - 1609-0985

IS - 5

ER -