Retrospective 3D registration of trabecular bone MR images for longitudinal studies

Abstract

Purpose: To evaluate an automatic 3D registration algorithm for serial high-resolution images of trabecular bone (TB) in studies designed to evaluate the response of the trabecular architecture to intervention or disease progression.

Materials and methods: An efficient algorithm for registering high-resolution 3D images of TB is presented. The procedure identifies the six parameters of rigid displacement between two scans performed at different timepoints. By assuming a relatively small through-plane rotation, considerable time is saved by combining the results of a collection of regional 2D registrations throughout the TB region of interest (ROI). The algorithm was applied to 26 pairs of MR images acquired 6 months apart. Reproducibility of local TB structural parameters (plate, rod, and junction density) computed in manually selected regions were compared between baseline and registered follow-up images.

Results: All 26 registrations were completed successfully in less than 30 seconds per image pair. The resampled follow-up images agreed with baseline to around one pixel throughout the volume at 137 x 137 x 410 microm(3) image resolution. Structural parameters in each region correlated well from baseline to follow-up with intraclass correlation coefficients ranging between 85%-97% for TB plate density. Interregional variations in the parameters were large as compared with intraregion reproducibility.

Conclusion: The proposed algorithm was successful in automatically registering baseline and follow-up TB images in a translational study, and may be useful in regional analyses in longitudinal MR studies of TB architecture.

Fig 1

Automatic trabecular bone ROI segmentation procedure illustrated for the distal tibia. (a) Manually selected rectangular region centered on the ROI; (b) Overlapping grid pattern for applying local threshold; (c) Initial local threshold mask, roughly identifying cortical shell; (d) Dilation of mask in (c) to avoid leakage; (e) Selection of main connected component; (f) Final ROI after filling small holes.

Fig 2

a) Sagittal localizer of the distal tibia with high-resolution imaging slab indicated; b) results of step 2 of the registration procedure showing cross-sectional area versus slice number in baseline and follow-up scan.

Fig 3

Baseline (left) and follow-up (right) images of the distal tibia. In steps 3 and 4 of the registration algorithm, two-dimensional rectangular regions in the baseline image are matched to patches in the follow-up image using the pattern of the trabecular bone network. Four adjacent patches are used to verify the success of the registration.

Fig 4

Five regions manually selected in the baseline scan (a). Those regions were transformed to the non-resampled follow-up (b) and copied to the resampled follow-up (c).

Fig 5

Distributions of 3D registration parameters for 26 subjects scanned at two time points, and distribution of the number of successes per 100 regional registration trials.

Fig 6

Single slice at baseline (a), follow-up (b), and resampled follow-up (c). The left magnification shows a region that has matching trabecular pattern in all three images, whereas the right magnification shows a region where the trabecular network matches in the baseline and resampled follow-up, but not in the non-resampled follow-up. This behavior reflects the through-plane tilt (~3 degrees in this case).

Fig 7

Follow-up vs. baseline correlation plots of plate, rod, and junction densities within each region.

Fig 8

Pairwise comparison plots between plate, rod, and junction densities (baseline data only) for each region.