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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.
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页码:1500 / 1511
页数:12
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