The influence of reconstruction algorithm on the measurement of airway dimensions using computed tomography
Bibliographic record
Abstract
The assessment of airway dimensions is important in understanding the pathophysiology of various lung diseases. A number of methods have been developed to measure airways on computed tomography, but no study has been done to validate the different CT scanning techniques, CT scanners, and reconstruction algorithms. In our study, we constructed an artificial "airway" and "lung" phantom using hollow plastic tubes and foam blocks. The phantom was CT scanned using axial or helical techniques, and the images were reconstructed using a very high spatial frequency algorithm, a high spatial frequency algorithm, or a low spatial frequency algorithm. Custom software was then used to analyze the "airways" and measure lumen area (Ai) and "airway" wall area (Aaw). WA% (WA% = 100 x Aaw / (Ai + Aaw)) was also calculated. The cross-sectional area of the lumen and wall of the plastic tubes were measured using an optical micrometer. CT measurements of airway dimensions were virtually identical, comparing axial and helical techniques, and comparing a single-slice CT scanner to a multi-slice CT scanner. Using the plastic tube measurements as a "gold standard", Ai was estimated better with the very high or high spatial frequency algorithm (4.1 and 7.4 % error) vs. low spatial frequency algorithm (10.4% error). Aaw was better estimated with the low or high special frequency algorithm (3.8% and 6.1%) vs. very high spatial frequency algorithm (12.9%), and WA% was better estimated with the high or low spatial frequency algorithm (3.5% and 5.1%) vs. very high spatial frequency algorithm (7.3%). Based on these results, we recommend the high spatial frequency algorithm for the CT measurement of airway dimensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".