Iodine potassium iodide improves the contrast‐to‐noise ratio of micro‐computed tomography images of the human middle ear
Bibliographic record
Abstract
High-resolution imaging of middle-ear geometry is necessary for finite-element modeling. Although micro-computed tomography (microCT) is widely used because of its ability to image bony structures of the middle ear, it is difficult to visualize soft tissues - including the tympanic membrane and the suspensory ligaments/tendons - because of lack of contrast. The objective of this research is to quantitatively evaluate the efficacy of iodine potassium iodide (IKI) solution as a contrast agent. Six human temporal bones were used in this experiment, which were obtained in right-left pairs, from three cadaveric heads. All bones were fixed using formaldehyde. Three bones (one from each pair) were stained in IKI solution for 2 days, whereas the other three were not stained. Samples were scanned using a microCT system at a resolution of 20 μm. Eight soft tissues in the middle ear were segmented: anterior mallear ligament, incudomallear joint, lateral mallear ligament, posterior incudal ligament, stapedial annular ligament, stapedius muscle, tympanic membrane and tensor tympani muscle. Contrast-to-noise ratios (CNRs) of each soft tissue were calculated for each temporal bone. Combined CNRs of the soft tissues in unstained samples were 6.1 ± 3.0, whereas they were 8.1 ± 2.7 in stained samples. Results from Welch's t-test indicate significant difference between the two groups at a 95% confidence interval. Results for paired t-tests for each of the individual soft tissues also indicated significant improvement of contrast in all tissues after staining. Relatively large soft tissues in the middle ear such as the tympanic membrane and the tensor tympani muscle were impacted by staining more than smaller tissues such as the stapedial annular ligament. The increase in contrast with IKI solution confirms its potential application in automatic segmentation of the middle-ear soft tissues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".