Modeling Respiratory Motion for Cancer Radiation Therapy Based on Patient-Specific 4DCT Data

  • Jaesung Eom
  • Chengyu Shi
  • Xie George Xu
  • Suvranu De
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)


Prediction of respiratory motion has the potential to substantially improve cancer radiation therapy. A nonlinear finite element (FE) model of respiratory motion during full breathing cycle has been developed based on patient specific pressure-volume relationship and 4D Computed Tomography (CT) data. For geometric modeling of lungs and ribcage we have constructed intermediate CAD surface which avoids multiple geometric smoothing procedures. For physiologically relevant respiratory motion modeling we have used pressure-volume (PV) relationship to apply pressure loading on the surface of the model. A hyperelastic soft tissue model, developed from experimental observations, has been used. Additionally, pleural sliding has been considered which results in accurate deformations in the superior-inferior (SI) direction. The finite element model has been validated using 51 landmarks from the CT data. The average differences in position is seen to be 0.07 cm (SD = 0.20 cm), 0.07 cm (0.15 cm), and 0.22 cm (0.18 cm) in the left-right, anterior-posterior, and superior-inferior directions, respectively.


Respiratory Motion Compute Tomography Data Nonlinear Finite Element Deformable Image Registration Laplacian Smoothing 
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Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jaesung Eom
    • 1
  • Chengyu Shi
    • 2
  • Xie George Xu
    • 1
  • Suvranu De
    • 1
  1. 1.Department of Mechanical, Aerospace and Nuclear EngineeringRensselaer Polytechnic InstituteTroyUSA
  2. 2.Department of Radiation OncologyUniversity of Texas Health Science Center at San AntonioSan AntonioUSA

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