View in EDS HTML Full Text PDF Full Text

Modeling lung deformation: A combined deformable image registration method with spatially varying Young's modulus estimates.

Saved in:
Bibliographic Details
Title: Modeling lung deformation: A combined deformable image registration method with spatially varying Young's modulus estimates.
Authors: Li, Min1, Castillo, Edward2, Zheng, Xiao Lin3, Luo, Hong Yan3, Castillo, Richard4, Wu, Yi5, Guerrero, Thomas2
Source: Medical Physics. Aug2013, Vol. 40 Issue 8, p081902-N.PAG. 10p.
Subjects: Lung abnormalities, Image registration, Medical imaging systems, Parameter estimation, Cancer radiotherapy, Respiration, Biomechanics, Computed tomography, Radiation doses
Abstract: Purpose: Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution. Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs. Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively. Conclusions: The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 89546591
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Modeling lung deformation: A combined deformable image registration method with spatially varying Young's modulus estimates.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Min%22">Li, Min</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Castillo%2C+Edward%22">Castillo, Edward</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Xiao+Lin%22">Zheng, Xiao Lin</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Luo%2C+Hong+Yan%22">Luo, Hong Yan</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Castillo%2C+Richard%22">Castillo, Richard</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yi%22">Wu, Yi</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Guerrero%2C+Thomas%22">Guerrero, Thomas</searchLink><relatesTo>2</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Aug2013, Vol. 40 Issue 8, p081902-N.PAG. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Lung+abnormalities%22">Lung abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+radiotherapy%22">Cancer radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Respiration%22">Respiration</searchLink><br /><searchLink fieldCode="DE" term="%22Biomechanics%22">Biomechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Radiation+doses%22">Radiation doses</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution. Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs. Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively. Conclusions: The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=89546591
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1118/1.4812419
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 081902
    Subjects:
      – SubjectFull: Lung abnormalities
        Type: general
      – SubjectFull: Image registration
        Type: general
      – SubjectFull: Medical imaging systems
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Cancer radiotherapy
        Type: general
      – SubjectFull: Respiration
        Type: general
      – SubjectFull: Biomechanics
        Type: general
      – SubjectFull: Computed tomography
        Type: general
      – SubjectFull: Radiation doses
        Type: general
    Titles:
      – TitleFull: Modeling lung deformation: A combined deformable image registration method with spatially varying Young's modulus estimates.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Min
      – PersonEntity:
          Name:
            NameFull: Castillo, Edward
      – PersonEntity:
          Name:
            NameFull: Zheng, Xiao Lin
      – PersonEntity:
          Name:
            NameFull: Luo, Hong Yan
      – PersonEntity:
          Name:
            NameFull: Castillo, Richard
      – PersonEntity:
          Name:
            NameFull: Wu, Yi
      – PersonEntity:
          Name:
            NameFull: Guerrero, Thomas
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: Aug2013
              Type: published
              Y: 2013
          Identifiers:
            – Type: issn-print
              Value: 00942405
          Numbering:
            – Type: volume
              Value: 40
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Medical Physics
              Type: main
ResultId 1