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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Physics-Informed In-Silico Dynamic Computed Tomography of Human Lungs: Generation, Evaluation, and Refinement.

Sunder Neelakantan1,2, Kyle J Myers3,2, Reza Avazmohammadi4,2

  • 1Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843.

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|August 13, 2025
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Summary

Finite element (FE) simulations generated in-silico CT images to validate image registration (IR) accuracy for lung function assessment. This method accurately estimates lung displacement and volumetric strain, improving clinical imaging analysis for lung diseases.

Keywords:
computed tomographyimage registrationin-silico imageslungsphysics-informed motion registration

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Pulmonary Medicine

Background:

  • Lung injuries cause uneven ventilation, and current lung function tests lack regional detail.
  • Dynamic medical imaging and image registration (IR) can assess lung movement, but validation is challenging.
  • In-silico images from finite element (FE) simulations offer a way to verify IR results.

Purpose of the Study:

  • To use in-silico CT images from FE simulations to evaluate the accuracy of an IR method for lung parenchyma.
  • To compare displacement and volumetric strain estimations between FE simulations and IR on actual and in-silico CT images.

Main Methods:

  • Reconstructed lungs from human 4DCT images to create an FE mesh.
  • Performed in-silico simulations using the lung FE mesh to generate in-silico dynamic CT images.
  • Executed IR on both actual and in-silico images and compared results to FE simulations.

Main Results:

  • FE simulations and IR showed good agreement in lung displacement estimation.
  • The greatest displacement difference between IR and FE simulations was 2.7 mm at the main bronchi.
  • Higher resolution in-silico images improved volumetric strain contour agreement.

Conclusions:

  • FE simulation-derived in-silico CT images effectively validate IR methods for lung analysis.
  • This approach can optimize medical imaging techniques for studying lung diseases.
  • The method holds potential for improved regional lung function assessment in clinical settings.