Physics-informed in-silico dynamic CT of human lungs: Generation, evaluation, and refinement.

Lung injuries lead to heterogeneous ventilation 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 provide a powerful tool to estimate the kinematic behavior of lung parenchyma in vivo. However, the difficulty of validating the displacement and deformation estimated by image registration has prevented widespread clinical adoption. In-silico phantoms, building on finite element (FE) simulations, provide a method to verify the results estimated through image registration. In this study, we used dynamic CT (4DCT) images from human patients to reconstruct the lungs and generate an FE mesh. In-silico simulation was performed using the lung FE mesh, and the results were used to generate dynamic in-silico CT phantoms matching the 4DCT resolution. Image registration was performed on the original and in-silico phantom images, and the results were compared to the FE simulation results. Results indicated a good agreement between the displacements resulting from the three methods, with an error of 2.7 mm between image registration and the finite element simulations. The volumetric strain comparisons between 4DCT and the phantom were used to assess the phantom?s accuracy. A new set of phantoms 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.