Middle and Inner Ear 3D Reconstructions using High Resolution X‐ray Micro‐Tomography
Bibliographic record
Abstract
Introduction The temporal bone is a complex 3D structure that is difficult to teach to ENT residents. Learning about this anatomy from classical textbooks requires considerable intellectual gymnastics to deal with this spatially complex structure. We present a new way for teaching this anatomy with high resolution 3D reconstructions using X‐ray micro‐tomography. Materials and Methods 10 human petrous parts of temporal bones were fixed with Winckler's solution. CT images were acquired with a CT scanner (Skyscan 1076, Bruker, Kontich, Belgium). The camera was continuously rotated by 180° and the following parameters were used for acquisition: 50 kV, 0.5 mm Al filter, 200 A source current, 35 m isotropic resolution, 180 ms exposure time, and 0.7° rotation step. The projections were reconstructed with a filtered backprojection algorithm in Skyscan software (NRecon, Skyscan). Reconstructed images were analyzed with ORS Visual software.(v1.5, Montreal), which allowed multiple display and post‐processing possibilities (3D VR; orthogonal/curved multiplanar reformation). Results and Discussion Three‐dimensional and multiplanar studies of the temporal bone morphology were possible with appropriate thresholding. The whole components of the middle ear (ossicles, tympanic cavity bony reliefs), the inner ear (cochlea, modiolus, cochlear canal, vestibule and semicircular canals) and their respective anatomic relationships are clearly visible. This method seems very effective in teaching this complex anatomy. Conclusion 3D reconstructions using high resolution X‐ray micro‐tomography aid comprehension of temporal bone complex anatomy. This precise knowledge will help residents to better understand otological surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".