Multi-modality CT imaging of human bone for improved validation of subject-specific finite element analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».