Visualization of 3D osteon morphology by synchrotron radiation micro‐CT
Bibliographic record
Abstract
Cortical bone histology has been the subject of scientific inquiry since the advent of the earliest microscopes. Histology - literally the study of tissue - is a field nearly synonymous with 2D thin sections. That said, progressive developments in high-resolution X-ray imaging are enabling 3D visualization to reach ever smaller structures. Micro-computed tomography (micro-CT), employing conventional X-ray sources, has become the gold standard for 3D analysis of trabecular bone and is capable of detecting the structure of vascular (osteonal) porosity in cortical bone. To date, however, direct 3D visualization of secondary osteons has eluded micro-CT based upon absorption-derived contrast. Synchrotron radiation micro-CT, through greater image quality, resolution and alternative contrast mechanisms (e.g. phase contrast), holds great potential for non-destructive 3D visualization of secondary osteons. Our objective was to demonstrate this potential and to discuss areas of bone research that can be advanced through the application of this approach. We imaged human mid-femoral cortical bone specimens derived from a 20-year-old male (Melbourne Femur Collection) at the Advanced Photon Source synchrotron (Chicago, IL, USA) using the 2BM beam line. A 60-mm distance between the target and the detector was employed to enhance visualization of internal structures through propagation phase contrast. Scan times were 1 h and images were acquired with 1.4-μm nominal isotropic resolution. Computer-aided manual segmentation and volumetric 3D rendering were employed to visualize secondary osteons and porous structures, respectively. Osteonal borders were evident via two contrast mechanisms. First, relatively new (hypomineralized) osteons were evident due to differences in X-ray attenuation relative to the surrounding bone. Second, osteon boundaries (cement lines) were delineated by phase contrast. Phase contrast also enabled the detection of soft tissue remnants within the vascular pores. The ability to discern osteon boundaries in conjunction with vascular and cellular porosity revealed a number of secondary osteon morphologies and provided a unique 3D perspective of the superimposition of secondary osteons on existing structures. Improvements in resolution and optimization of the propagation phase contrast promise to provide further improvements in structural detail in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".