Columns of new bone tissue retain central pores that extend the vascular network to nourish bone cells. Cortical porosity increases and converges during aging, causing bone loss and increased fracture risk. Micro-computed tomography provides a three-dimensional reconstruction of this complex, highly interconnected pore network, and can visualize how porosity changes due to varying mechanical and physiological stimuli over the lifespan. Most segmentation methods for microCT images separate bone and space by globally restricting the visible pixel brightness range. This approach excludes faintly outlined pores and trabecular margins and cannot distinguish cortical pores and trabecular cavities within the ambiguous endocortical region. In the current research, an age series of cadaveric human midshaft fourth ribs and distal femoral necks were imaged in a HeliScan Micro-CT at voxel size 6.41 µm for a 10 mm sample thickness. Slice histograms were normalized and restricted to a set pixel brightness range in ImageJ. A CTan macro imposed a Gaussian blur, an adaptive threshold mean of the minimum and maximum pixel brightness, and despeckling that extracted virtually all visible pores. After axial reslicing and femoral neck column merging in AvizoFire 8.1, a custom CTan macro automatically estimated the endosteal boundary to separate marrow-adjacent pores from trabecular cavities, generating a stack of isolated cortical pore spaces. Preliminary analysis of the rib of a healthy 34-year-old male indicates 16.3 percent cortical porosity by volume at the midshaft, with the largest cortical pore measuring 0.082 mm3. (publisher abstract modified)
Image Processing Techniques for Extracting Complex Three-Dimensional Cortical Pore Networks From High-Resolution Micro-Computed Tomography (Micro-CT) Images of the Human Femoral Neck and Rib
NCJ Number
254205
Journal
American Journal of Physical Anthropology Volume: 168 Dated: 2019 Pages: 46-46
Date Published
2019
Length
1 page
Annotation
The reported project developed custom macros for ImageJ (NIH) and CTan (Bruker) that overcome identified image processing challenges.
Abstract
Date Published: January 1, 2019