Multi-modality CT imaging of human bone for improved validation of subject-specific finite element analysis
Bibliographic record
Abstract
INTRODUCTION Finite element analysis (FE) is a promising alternative to dual-energy x-ray absorptiometry for improved prediction of fracture risk as FE can incorporate bone density, geometry, and microarchitecture [1]. However, the accuracy of FE models is heavily influenced by the spatial resolution of the CT scan [2]. In this study we quantify the architectural differences in trabecular bone between different modalities, specifically high-resolution peripheral quantitative computed tomography (HR-pQCT) and micro-computed tomography (μCT), and measure their effect on the resulting FE models. METHODS Two cadaveric, human distal tibiae were imaged using HR-pQCT (XtremeCT, Scanco Medical, Switzerland), with a voxel size of 82μm. After imaging, four cubes, 10mm edge length, were cut and scanned using μCT (μCT-35, Scanco Medical, Switzerland.), with a voxel size of 20μm. During a sensitivity analysis, the μCT image data was segmented using a threshold based technique and resampled to voxel sizes of 40μm, 60μm, and 80μm to assess the effect of voxel size on the FE results. The FE results were statistical compared with a one-way repeated measures ANOVA and a Tukey’s post-hoc test. Based on the known location of each cube recorded during the cutting process, the μCT data was manually aligned to the larger HR-pQCT image, where mutual information registration was applied for accurate alignment. Virtual cubes were extracted from the registered HR-pQCT data. All cube image data was rescaled to preserve the bone volume/total volume ratio. Subsequently, all image data was converted to hexahedron elements for FE analysis and subjected to 1% uniaxial compression (FAIM v6.0, Numerics88) in the x-, y-, and z-directions. The resulting FE data was compared with a two-way ANOVA, with Bonferroni multiple comparisons test. RESULTS The sensitivity analysis found no statistically significant differences in reaction force between any cubes with different resolutions when compressed in the z-direction. The mean percent error, when compared to the 20μm cube, for the 40μm, 60μm, and 80μm cubes was 0.06%, 0.45%, and 0.76%, respectively. In the y-direction, there were significant differences between the 20μm and the 80μm (p 0.05, 0.14% error.) Figure 1. Registered distal tibia (HR-pQCT - red) to the μCT acquired cubes (yellow). The registered image appears green in overlap. DISCUSSION AND CONCLUSIONS It was shown there is little difference in the FE results loaded in the z-direction upon rescaling μCT data (mean percent errors less than 0.8%). However, there are large changes in the non-axial (i.e. x and y) directions. It is recommended that μCT data be rescaled up to 40μm. In addition, it was shown that μCT data could be successfully registered to HR-pQCT (see figure 1). For a given resolution, μCT data is only comparable to HR-pQCT data in the z-direction. In conclusion, HR-pQCT may not be an ideal substitute for μCT micro-architectural data. This study improves our understanding on the use of HR-pQCT imaging for quantification of bone micro-architecture.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".