Thyroid Shields versus z-Axis Automatic Tube Current Modulation for Dose Reduction at Neck CT
Bibliographic record
Abstract
PURPOSE: To assess the effectiveness of in-plane bismuth thyroid shields and a z-axis automatic tube current modulation (ATCM) technique with respect to dose reduction and image noise in the thyroid and other regional tissues during neck computed tomography (CT). MATERIALS AND METHODS: Because this was a phantom study, neither institutional review board approval nor HIPAA compliance was required. A female phantom, thyroid shields, and an eight-section CT scanner were used. Radiation dose was measured by using thermoluminescent dosimetry (TLD) chips placed in the phantom's thyroid, cervical bone marrow, and soft tissues. Scans were performed by using fixed tube current and a z-axis ATCM technique with and without shields. Image noise was quantified as the standard deviation of the attenuation value (in Hounsfield units) on CT images. RESULTS: Thyroid dose was 76.9 mGy with an unshielded fixed tube current technique. Use of shields and ATCM reduced this dose to 44.7 mGy (42% reduction, P < .001) and 17.0 mGy (78% reduction, P < .001), respectively. The combination of shields and ATCM further reduced this dose to between 11.9 and 12.9 mGy (83%-85% reduction, P < .001), depending on the shield's presence in the scout scan. ATCM resulted in minimized tube current throughout the neck, which reduced dose-length product across the entire scan volume by 60% (926 vs 368 mGy . cm). Thyroid bed noise was lowest (3.9 HU) during the fixed tube current technique without shields, with noise slightly higher (6.9 HU) with the unshielded ATCM technique (P < .001). Thyroid image noise was markedly higher when using shields (P < .001), with noise measuring between 74 and 113 HU for fixed tube current and ATCM scans. CONCLUSION: z-Axis ATCM is more effective than shields at reducing thyroid radiation dose during neck CT. Shields combined with ATCM slightly further reduces dose; however, this is associated with higher image noise.
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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".