In-silico CT lung phantom generated from finite-element mesh

Sunder Neelakantan, Tanmay Mukherjee, Bradford J. Smith, Kyle Myers, Rahim Rizi, Reza Avazmohammadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Several lung diseases lead to alterations in regional lung mechanics, including ventilator-and radiation-induced lung injuries. Such alterations can lead to localized underventilation of the affected areas, resulting in the overdistension of the surrounding healthy regions. Thus, there has been growing interest in quantifying the dynamics of the lung parenchyma using regional biomechanical markers. Image registration through dynamic imaging has emerged as a powerful tool to assess lung parenchyma's kinematic and deformation behaviors during respiration. However, the difficulty in validating the image registration estimation of lung deformation, primarily due to the lack of ground-Truth deformation data, has limited its use in clinical settings. To address this barrier, we developed a method to convert a finite-element (FE) mesh of the lung into a phantom computed tomography (CT) image, advantageously possessing ground-Truth information included in the FE model. The phantom CT images generated from the FE mesh replicated the geometry of the lung and large airways that were included in the FE model. Using spatial frequency response, we investigated the effect of "imaging parameters"such as voxel size (resolution) and proximity threshold values on image quality. A series of high-quality phantom images generated from the FE model simulating the respiratory cycle will allow for the validation and evaluation of image registration-based estimations of lung deformation. In addition, the present method could be used to generate synthetic data needed to train machine-learning models to estimate kinematic biomarkers from medical images that could serve as important diagnostic tools to assess heterogeneous lung injuries.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2024
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsJeffrey H. Siewerdsen, Maryam E. Rettmann
ISBN (Electronic)9781510671607
StatePublished - 2024
EventMedical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 19 2024Feb 22 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/TerritoryUnited States
CitySan Diego


  • CT imaging
  • image registration
  • in-silico phantom
  • lung phantom

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'In-silico CT lung phantom generated from finite-element mesh'. Together they form a unique fingerprint.

Cite this