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
  • [31] 4D CT image acquisition errors in SBRT of liver identified using correlation
    Szegedi, Martin
    Sarkar, Vikren
    Rassiah-Szegedi, Prema
    Wang, Brian
    Huang, Y. Jessica
    Zhao, Hui
    Salter, Bill
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2012, 13 (01): : 164 - 173
  • [32] Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis
    Eom, Jaesung
    Xu, Xie George
    De, Suvranu
    Shi, Chengyu
    MEDICAL PHYSICS, 2010, 37 (08) : 4389 - 4400
  • [33] Dose Accumulation based on Optimized Motion Field Estimation using Non-Linear Registration in Thoracic 4D CT Image Data
    Werner, R.
    Ehrhardt, J.
    Schmidt-Richberg, A.
    Bodmann, B.
    Cremers, F.
    Handels, H.
    WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, VOL 25, PT 4: IMAGE PROCESSING, BIOSIGNAL PROCESSING, MODELLING AND SIMULATION, BIOMECHANICS, 2010, 25 : 950 - 953
  • [34] Metallic Artifact Reduction in Midfacial CT Scans Using Patient-Specific Polymer Implants Enhances Image Quality
    Lommen, Julian
    Schorn, Lara
    Sproll, Christoph
    Kerkfeld, Valentin
    Aksu, Adem
    Reinauer, Frank
    Kuebler, Norbert R.
    Budach, Wilfried
    Rana, Majeed
    Tamaskovics, Balint
    JOURNAL OF PERSONALIZED MEDICINE, 2023, 13 (02):
  • [35] Lung tumor segmentation in 4D CT images using motion convolutional neural networks
    Momin, Shadab
    Lei, Yang
    Tian, Zhen
    Wang, Tonghe
    Roper, Justin
    Kesarwala, Aparna H.
    Higgins, Kristin
    Bradley, Jeffrey D.
    Liu, Tian
    Yang, Xiaofeng
    MEDICAL PHYSICS, 2021, 48 (11) : 7141 - 7153
  • [36] Evaluation of Patient-Specific Cranial Implant Design Using Finite Element Analysis
    Huys, Stijn E. F.
    Van Gysel, Anke
    Mommaerts, Maurice Y.
    Vander Sloten, Jos
    WORLD NEUROSURGERY, 2021, 148 : 198 - 204
  • [37] Patient-specific QA using 4D Monte Carlo phase space predictions and EPID dosimetry
    Popescu, I. A.
    Atwal, P.
    Lobo, J.
    Lucido, J.
    McCurdy, B. M. C.
    8TH INTERNATIONAL CONFERENCE ON 3D RADIATION DOSIMETRY (IC3DDOSE), 2015, 573
  • [38] Differences in respiratory-induced pancreatic tumor motion between 4D treatment planning CT and daily cone beam CT, measured using intratumoral fiducials
    Lens, Eelco
    van der Horst, Astrid
    Kroon, Petra S.
    van Hooft, Jeanin E.
    Fajardo, Raquel Davila
    Fockens, Paul
    van Tienhoven, Geertjan
    Bel, Arjan
    ACTA ONCOLOGICA, 2014, 53 (09) : 1257 - 1264
  • [39] Geographic miss of lung tumours due to respiratory motion: a comparison of 3D vs 4D PET/CT defined target volumes
    Callahan, Jason
    Kron, Tomas
    Siva, Shankar
    Simoens, Nathalie
    Edgar, Amanda
    Everitt, Sarah
    Schneider, Michal E.
    Hicks, Rodney J.
    RADIATION ONCOLOGY, 2014, 9
  • [40] Direction-Dependent Regularization for Improved Estimation of Liver and Lung Motion in 4D Image Data
    Schmidt-Richberg, Alexander
    Ehrhardt, Jan
    Werner, Rene
    Handels, Heinz
    MEDICAL IMAGING 2010: IMAGE PROCESSING, 2010, 7623