Toward accurate regional lung strain quantification from microCT data: insights and limitations of digital volume...

The biomechanics of the lung plays a crucial role in respiratory function, and its alteration is closely linked to the onset and progression of lung diseases. Quantifying local strain within lung tissue is essential for understanding how lung mechanics influence pulmonary function and disease progression. Digital Volume Correlation (DVC) provides a non-destructive method to quantify 3D strain fields in biological tissues, but its accuracy depends on multiple parameters, which remain poorly characterized in soft tissues. In this study, we evaluated the accuracy of DVC, using both local and global approaches, for lung tissue imaged with high-resolution contrast-enhanced computed tomography (HR CECT) and assessed its potential application throughout a range of lower resolution and noisier data towards HR ex vivo and in vivo microCT data. We investigated the effects of DVC settings, lung microstructure, and input image quality, including noise level and voxel size. Synthetic deformations of known magnitudes (1-5%) were applied to optimize subvolume and mesh sizes, with mean metric values and correlation residuals used to evaluate registration quality. Strain measurements from global DVC were highly sensitive to mesh size, with optimal meshes yielding robust estimates, while larger meshes reduced accuracy. Correlation residuals reliably identified the optimal mesh size as a global registration metric. Adding Gaussian noise demonstrated that higher noise levels reduced DVC accuracy, particularly at larger deformations and with non-optimal meshes. Downscaling imaging data increased voxel size in a range from 3.8 μm to 30.4 μm left central strain distributions largely unchanged, but induced boundary-related errors with regions of high strain intensity near sample boundaries highlighting the need for sufficiently large volumes of interest at large voxel sizes for DVC-based strain analysis. Within these limitations, DVC effectively captured regional lung strain, supporting its applicability from HR CECT data to ex vivo HR microCT. Our findings provide critical insights into how DVC operational parameters, across both local and global approaches, tissue microarchitecture and input imaging quality affect the accuracy of strain measurements in soft tissues. By demonstrating the feasibility of DVC for lung strain analysis using microCT data, this study provides a foundation for further research into the use of strain measurements to characterize lung biomechanics in animal models at study endpoints with ex vivo HR microCT.