Abstract
Strain is a robust indicator of bone failure initiation. Previous work has demonstrated the measurement of vertebral trabecular bone strain by Digital Volume Correlation (DVC) of µCT scan in both a loaded and an unloaded configuration. This project aims to improve previous strain measurement methods relying on image registration, improving resolution to resolve trabecula level strain and to improve accuracy by applying feature based registration algorithms to µCT images of vertebral trabecular bone to quantify strain. It is hypothesised that extracting reliable corresponding feature points from loaded and unloaded µCT scans can be used to produce higher resolution strain fields compared to DVC techniques.
The feature based strain calculation algorithm has two steps: 1) a displacement field is calculated by finding corresponding feature points identified in both the loaded and unloaded µCT scans 2) strain fields are calculated from the displacement fields. Two methods of feature point extraction, Scale Invariant Feature Transform (SIFT) and Skeletonisation, were applied to unloaded (fixed) and loaded (moving) µCT images of a rat tail vertebra. Spatially non-uniform displacement fields were generated by automatically matching corresponding feature points in the unloaded and loaded scans. The Thin Plate Spline method and a Moving Least Squares Meshless Method were both tested for calculating strain from the displacement fields. Verification of the algorithms was performed by testing against known artificial strain/displacement fields. A uniform and a linearly varying 2% compressive strain field were applied separately to an unloaded 2D sagittal µCT slice to simulate the moving image.
SIFT was unable to reliably match identified feature points leading to large errors in displacement. Skeletonisation generated a more accurate and precise displacement field. TPS was not tolerant to small displacement field errors, which resulted in inaccurate strain fields. The Meshless Methods proved much more resilient to displacement field errors. The combination of Skeletonisation with the Meshless Method resulted in best performance with an accuracy of −405µstrain and a detection limit of 1210µstrain at a strain resolution of 221.5µm. The DVC algorithm verified using the same validation test yielded a similar detection limit (1190µstrain), but with a lower accuracy for the same test (2370µstrain) for a lower resolution strain field (770µm) (Hardisty, 2009).
The Skeletonisation algorithm combined with the Meshless Method calculated strain at a higher resolution, but with a similar detection limit, to that of traditional DVC methods. Future improvements to this method include the implementation of subpixel feature point identification and adapting this method of strain measurement into a 3D domain. Ultimately, a hybrid DVC/feature registration algorithm may further improve the ability to measure trabecular bone strain using µCT based image registration.
