Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

B. Fuerst, T. Mansi, F. Carnis, M. Saelzle, T. Zhang, J. Declerck, T. Boettger, J. Bayouth, N. Navab, A. Kamen
Patient-Specific Biomechanical Model for the Prediction of Lung Motion from 4D CT Images
IEEE Transactions on Medical Imaging, 2014. (bib)

Abstract—This article presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from Computed Tomography (CT) images at endexpiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40+/-1.0 mm over the entire respiratory cycle. The estimated 3D lung motion may constitute as an advanced 3D surrogate for more accurate medical image reconstruction and patient respiratory analysis.
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