Effect of Voxel Size on Finite-Element Analysis of Micro-CT Derived Bone Sample
Bibliographic record
Abstract
Introduction: Bone strength is dependent on the structural parameters of thetrabecular micro-architecture1. A method to estimate bone strength is finite element(FE) analysis of the bone micro-architecture2. Quantification of structural parameters3and FE analysis results are dependent on the image resolution2. This study used microcomputedtomography (micro-CT) to investigate how voxel size affects the accuracy oftrabecular bone measurements, particularly regarding how it relates to FE modelingprediction of bone strength.Methods: Cadaveric bovine cubic bones were imaged at an isotropic voxel sizeof 20mm using a micro-CT scanner (Micro-CT35). Images were segmented using athreshold based technique and re-scaled to voxel sizes 2-4 times larger (40mm-80mm)than the original images. Three-dimensional analyses of trabecular bone propertieswere quantified within the images of the bone cubes. Image voxels were converted tohexahedral elements for FE analysis. Uniaxial 1% compression test was performed onall data (FAIM 5.4). Nodes on the bottom surface were fixed while the top surface wassubjected to compression. No constraints were applied to the x and y directions.Results: Trabecular number (TbN) measurements increased linearly with increasingresolution. There was a 22.11% difference between trabecular number values at20mm versus 80mm. All other structural parameters were not statistically significantbetween different image resolutions (p > 0.05). For FE analysis, there was a 3.05%percent difference for mean von-Mises Stress at 20mm versus 80mm. Total reactionforce between 20mm and 80mm differed by 0.484%. Maximum von-Mises stress wasstatistically significantly different between 20mm and 80mm.Conclusion: All structural parameters except TbN measured at 20mm are comparableto 80mm. Similarly, bone strength estimates through FE analysis at 20mm arecomparable to 80mm. It is unlikely that TbN influenced the bone strength estimates.These results will allow for non-invasive estimate of bone strength with advancedclinical CT scanners.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".