Patient-specific finite element modeling of respiratory lung motion using 4D CT image data

被引:115
|
作者
Werner, Rene [1 ]
Ehrhardt, Jan [1 ]
Schmidt, Rainer [2 ]
Handels, Heinz [1 ]
机构
[1] Univ Med Ctr Hamburg Eppendorf, Dept Med Informat, D-20246 Hamburg, Germany
[2] Univ Med Ctr Hamburg Eppendorf, Dept Radiotherapy & Radiooncol, D-20246 Hamburg, Germany
关键词
biomechanics; computerised tomography; finite element analysis; lung; medical image processing; optimisation; pneumodynamics; radiation therapy; tumours; INSPIRATION BREATH-HOLD; ORGAN MOTION; DEFORMABLE REGISTRATION; RADIATION-THERAPY; TECHNICAL NOTE; TUMOR MOTION; CANCER; RECONSTRUCTION; RADIOTHERAPY; NONLINEARITY;
D O I
10.1118/1.3101820
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Development and optimization of methods for adequately accounting for respiratory motion in radiation therapy of thoracic tumors require detailed knowledge of respiratory dynamics and its impact on corresponding dose distributions. Thus, computer aided modeling and simulation of respiratory motion have become increasingly important. In this article a biophysical approach for modeling respiratory lung motion is described: Major aspects of the process of lung ventilation are formulated as a contact problem of elasticity theory which is solved by finite element methods; lung tissue is assumed to be isotropic, homogeneous, and linearly elastic. A main focus of the article is to assess the impact of biomechanical parameters (values of elastic constants) on the modeling process and to evaluate modeling accuracy. Patient-specific models are generated based on 4D CT data of 12 lung tumor patients. Simulated motion patterns of inner lung landmarks are compared with corresponding motion patterns observed in the 4D CT data. Mean absolute differences between model-based predicted landmark motion and corresponding breathing-induced landmark displacements as observed in the CT data sets are in the order of 3 mm (end expiration to end inspiration) and 2 mm (end expiration to midrespiration). Modeling accuracy decreases with increasing tumor size both locally (landmarks close to tumor) and globally (landmarks in other parts of the lung). The impact of the values of the elastic constants appears to be small. Outcomes show that the modeling approach is an adequate strategy in predicting lung dynamics due to lung ventilation. Nevertheless, the decreased prediction quality in cases of large tumors demands further study of the influence of lung tumors on global and local lung elasticity properties.
引用
收藏
页码:1500 / 1511
页数:12
相关论文
共 50 条
  • [1] Modeling respiratory motion for reducing motion artifacts in 4D CT images
    Zhang, Yongbin
    Yang, Jinzhong
    Zhang, Lifei
    Court, Laurence E.
    Balter, Peter A.
    Dong, Lei
    MEDICAL PHYSICS, 2013, 40 (04)
  • [2] Statistical Modeling of 4D Respiratory Lung Motion Using Diffeomorphic Image Registration
    Ehrhardt, Jan
    Werner, Rene
    Schmidt-Richberg, Alexander
    Handels, Heinz
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2011, 30 (02) : 251 - 265
  • [3] Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data
    Werner, Rene
    Ehrhardt, Jan
    Schmidt-Richberg, Alexander
    Heiss, Anabell
    Handels, Heinz
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2010, 5 (06) : 595 - 605
  • [4] Patient specific respiratory motion modeling using a limited number of 3D lung CT images
    Cui, Xueli
    Gao, Xin
    Xia, Wei
    Liu, Yangchuan
    Liang, Zhiyuan
    BIO-MEDICAL MATERIALS AND ENGINEERING, 2014, 24 (06) : 3113 - 3120
  • [5] Lung Respiratory Motion Estimation Based on Fast Kalman Filtering and 4D CT Image Registration
    Xue, Peng
    Fu, Yu
    Ji, Huizhong
    Cui, Wentao
    Dong, Enqing
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2021, 25 (06) : 2007 - 2017
  • [6] Patient specific respiratory motion modeling using a 3D patient's external surface
    Fayad, Hadi
    Pan, Tinsu
    Pradier, Olivier
    Visvikis, Dimitris
    MEDICAL PHYSICS, 2012, 39 (06) : 3386 - 3395
  • [7] Modeling respiratory lung motion - a biophysical approach using finite element methods
    Werner, Rene
    Ehrhardt, Jan
    Schmidt, Rainer
    Handels, Heinz
    MEDICAL IMAGING 2008: PHYSIOLOGY, FUNCTION, AND STRUCTURE FROM MEDICAL IMAGES, 2008, 6916
  • [8] Patient-specific finite element modeling for femoral bone augmentation
    Basafa, Ehsan
    Armiger, Robert S.
    Kutzer, Michael D.
    Belkoff, Stephen M.
    Mears, Simon C.
    Armand, Mehran
    MEDICAL ENGINEERING & PHYSICS, 2013, 35 (06) : 860 - 865
  • [9] Simulation of spatiotemporal CT data sets using a 4D MRI-based lung motion model
    Marx, Mirko
    Ehrhardt, Jan
    Werner, Rene
    Schlemmer, Heinz-Peter
    Handels, Heinz
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2014, 9 (03) : 401 - 409
  • [10] Analysis of free breathing motion using artifact reduced 4D CT image data
    Ehrhardt, Jan
    Werner, Rene
    Frenzel, Thorsten
    Lu, Wei
    Low, Daniel
    Handels, Heinz
    MEDICAL IMAGING 2007: IMAGE PROCESSING, PTS 1-3, 2007, 6512