Investigation of lung nodule detectability in low‐dose 320‐slice computed tomography
Bibliographic record
Abstract
Low-dose imaging protocols in chest CT are important in the screening and surveillance of suspicious and indeterminate lung nodules. Techniques that maintain nodule detectability yet permit dose reduction, particularly for large body habitus, were investigated. The objective of this study was to determine the extent to which radiation dose can be minimized while maintaining diagnostic performance through knowledgeable selection of reconstruction techniques. A 320-slice volumetric CT scanner (Aquilion ONE, Toshiba Medical Systems) was used to scan an anthropomorphic phantom at doses ranging from approximately 0.1 mGy up to that typical of low-dose CT (LDCT, approximately 5 mGy) and diagnostic CT (approximately 10 mGy). Radiation dose was measured via Farmer chamber and MOSFET dosimetry. The phantom presented simulated nodules of varying size and contrast within a heterogeneous background, and chest thickness was varied through addition of tissue-equivalent bolus about the chest. Detectability of a small solid lung nodule (3.2 mm diameter, -37 HU, typically the smallest nodule of clinical significance in screening and surveillance) was evaluated as a function of dose, patient size, reconstruction filter, and slice thickness by means of nine-alternative forced-choice (9AFC) observer tests to quantify nodule detectability. For a given reconstruction filter, nodule detectability decreased sharply below a threshold dose level due to increased image noise, especially for large body size. However, nodule detectability could be maintained at lower doses through knowledgeable selection of (smoother) reconstruction filters. For large body habitus, optimal filter selection reduced the dose required for nodule detection by up to a factor of approximately 3 (from approximately 3.3 mGy for sharp filters to approximately 1.0 mGy for the optimal filter). The results indicate that radiation dose can be reduced below the current low-dose (5 mGy) and ultralow-dose (1 mGy) levels with knowledgeable selection of reconstruction parameters. Image noise, not spatial resolution, was found to be the limiting factor in detection of small lung nodules. Therefore, the use of smoother reconstruction filters may permit lower-dose protocols without trade-off in diagnostic performance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".