Flat Panel Angiography in the Cross-Sectional Imaging of the Temporal Bone: Assessment of Image Quality and Radiation Dose Compared with a 64-Section Multisection CT Scanner
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cross-sectional imaging of the temporal bone is challenging because of the complexity and small dimensions of the anatomic structures. We evaluated the role of flat panel angiography in the cross-sectional imaging of the temporal bone by comparing its image quality and radiation dose with a 64-section multisection CT scanner. MATERIALS AND METHODS: We retrospectively collected 29 multisection CT and 29 flat panel angiography images of normal whole-head temporal bones. Image quality was assessed by 2 neuroradiologists, who rated the visualization of 30 anatomic structures with a 3-point ordinal scale. The radiation dose was assessed with an anthropomorphic phantom. RESULTS: Flat panel angiography showed better image quality than multisection CT in depicting the anterior and posterior crura of the stapes, the footplate of the stapes, the stapedius muscle, and the anterior ligament of the malleus (P < .05). In contrast, multisection CT showed better image quality than flat panel angiography in assessing the tympanic membrane, the bone marrow of the malleus and incus, the tendon of the tensor tympani, the interscalar septum, and the modiolus of the cochlea (P < .05). Flat panel angiography had a significantly higher overall image quality rating than multisection CT (P = .035). A reduction of the effective dose of approximately 40% was demonstrated for flat panel angiography compared with multisection CT. CONCLUSIONS: Flat panel angiography shows strengths and weaknesses compared with multisection CT. It is more susceptible to artifacts, but due to the higher spatial resolution, it shows equal or higher image quality in assessing some bony structures of diagnostic interest. The lower radiation dose is an additional advantage of flat panel angiography.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".