Physics-Informed In-Silico Dynamic Computed Tomography of Human Lungs: Generation, Evaluation, and Refinement.

Abstract

Lung injuries lead to heterogeneous ventilation behavior in the lung parenchyma, and conventional methods used to assess lung health, such as spirometry, fall short of providing regional information about lung function. Dynamic medical imaging and image registration offer a powerful tool for estimating the kinematic behavior of lung parenchyma in vivo. However, the difficulty of validating lung deformation estimated by image registration has curbed widespread adoption in the clinic. In-silico images, reconstructed from finite element (FE) simulations, provide a method to verify the results estimated through image registration (IR). Our objective in this study was to use in-silico computed tomography (CT) images, reconstructed from FE simulations, to assess the accuracy of an image registration method. In this study, we used dynamic CT (4DCT) images from human patients to reconstruct the lungs and generate an FE mesh. In-silico simulations were performed using the lung FE mesh, and the results were used to generate in-silico dynamic CT images matching the resolution of the actual 4DCT images. Image registration was performed on the actual and in-silico images, and the results were compared to those from the FE simulation. Results indicated good agreement in displacement estimated by the FE simulations and the image registration of the actual and in-silico CT images. The difference in predicted displacement image registration of the actual CT images and the FE simulations was greatest at the main bronchi, with a value of 2.7 mm. This result highlighted the effectiveness of the FE simulation-based method to generate in-silico CT images. The volumetric strain comparisons between actual 4DCT and the in-silico images were used to assess the method's accuracy. A new set of in-silico images was generated at a higher spatial resolution, resulting in improved agreement for the volumetric strain contours. We expect the method reported in this study to be applied to optimize medical imaging methods and investigate the behavior of various lung diseases under medical imaging.