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

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

doi:10.1117/12.3006973

29 March 2024 In-silico CT lung phantom generated from finite-element mesh

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

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 1292829 (2024) https://doi.org/10.1117/12.3006973
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sunder Neelakantan, Tanmay Mukherjee, Bradford J. Smith, Kyle Myers, Rahim Rizi, and Reza Avazmohammadi "In-silico CT lung phantom generated from finite-element mesh", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 1292829 (29 March 2024); https://doi.org/10.1117/12.3006973

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