Neutron microtomography to investigate the bone-implant interface—comparison with histological analysis
Bibliographic record
Abstract
Abstract Bone properties and especially its microstructure around implants are crucial to evaluate the osseointegration of prostheses in orthopaedic, maxillofacial and dental surgeries. Given the intrinsic heterogeneous nature of the bone microstructure, an ideal probing tool to understand and quantify bone formation must be spatially resolved. X-ray imaging has often been employed, but is limited in the presence of metallic implants, where severe artifacts generally arise from the high attenuation of metals to x-rays. Neutron tomography has recently been proposed as a promising technique to study bone-implant interfaces, thanks to its lower interaction with metals. The aim of this study is to assess the potential of neutron tomography for the characterisation of bone tissue in the vicinity of a metallic implant. A standardised implant with a bone chamber was implanted in rabbit bone. Four specimens were imaged with neutron tomography and subsequently compared to non-decalcified histology to stain soft and mineralised bone tissues, used here as a ground-truth reference. An intensity-based image registration procedure was performed to place the 12 histological slices within the corresponding 3D neutron volume. Significant correlations ( p < 0.01) were obtained between the two modalities for the bone-implant contact ( BIC ) ratio ( R = 0.77) and the bone content inside the chamber ( R = 0.89). The results indicate that mineralised bone tissue can be reliably detected by neutron tomography. However, the BIC ratio and bone content were found to be overestimated with neutron imaging, which may be explained by its sensitivity to non-mineralised soft tissues, as revealed by histological staining. This study highlights the suitability of neutron tomography for the analysis of the bone-implant interface. Future work will focus on further distinguishing soft tissues from bone tissue, which could be aided by the adoption of contrast agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".