Patient-specific biomechanical model for the prediction of lung motion from 4-D CT images

Bernhard Fuerst, Tommaso Mansi, Francois Carnis, Martin Sälzle, Jingdan Zhang, Jerome Declerck, Thomas Boettger, John Bayouth, Nassir Navab, Ali Kamen

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


This paper 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 end-expiration (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 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.

Original languageEnglish (US)
Article number6926856
Pages (from-to)599-607
Number of pages9
JournalIEEE transactions on medical imaging
Issue number2
StatePublished - Feb 1 2015
Externally publishedYes


  • Biomechanical modeling
  • lung
  • motion prediction
  • personalization
  • respiratory motion

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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