Preserving trabecular bone structure in gross specimens with paraffin wax for the study of osteoporosis
Marc A. Pizzimenti, Xiaoliu Zhang, Jessica E. Goetz, and Punam K. Saha,
Analytical strategies to interpret computed tomography data for the study of osteoporosis require gross anatomical specimens that closely approximate the bone marrow matrix while maintaining the structural integrity of the trabecular network. Here we investigated if paraffin, gelatin-matrix, or silicone polymer impregnated bone specimens maintained the trabecular network and bone density of specimens used for comparative studies in osteoporosis. Distal tibia specimens (n = 12) from cadaveric human legs were used for this study. Multi-row detector computed tomography (MDCT) scans were completed on each specimen, with selected specimens re-scanned using micro-computed tomography (µCT), at different times to evaluate trabecular integrity. Once scanned, bones were de-marrowed using a solvent-based procedure. De-marrowed specimens were then impregnated, under vacuum controlled conditions, with either a silicone polymer, gelatin matrix solution, or paraffin wax, and re-scanned. µCT analysis demonstrated a high Pearson correlation coefficient in trabecular bone network area density between the native and de-marrowed states (r = 0.99). Quantitatively, paraffin impregnated specimens demonstrated the highest congruence in MCDT bone volume fraction (r = 0.92) and trabecular network area (r = 0.94) measures. These results were more robust in the µCT data (bone volume fraction, r = 0.97; and trabecular network area, r = 0.99). Additionally, of the impregnating procedures, paraffin wax provided the best qualitative specimens for handling, storage, and processing time. These data suggest that paraffin demonstrates a space occupying matrix that effectively protects the trabecular network and maintains the ability to quantify important biomechanical measures for assessing bone integrity. The preserved specimens will serve as standard models for comparison when developing algorithms for studying osteoporosis.