LINEAR DIMENSIONS AND VOLUMES OF HUMAN LUNGS OBTAINED FROM CT IMAGES
Bibliographic record
Abstract
This work provides the results of a collaboration between the Human Monitoring Laboratory (HML) and the Centre Hospitalier de l'Université de Montréal (CHUM) in which CHUM provided CT lung image sets from 166 patients for the analysis of linear dimensions and lung volume. This work has shown that a large amount of data exists in the medical community that can be of value to the health physics community. The intent of this study was to determine the range of linear dimensional parameters that could be used for torso phantom development for males and females; understand and characterize the variability of linear lung dimensions for males and females; replace the brief table in ICRP 23 with more modern data for males and females; identify an empirical formula that would predict linear dimensions of human lungs from age, height and/or weight for males and females; characterize the left, right, and total lung volumes of males and females in this data set; and compare the lung volumes of males and females to published equations for determining lung volumes. It was found that linear dimensions of lungs are essentially independent of age, height, and weight, so predictive equations cannot be formulated; however, the ranges of those parameters have now been established for the population studied herein. The data presented here are more modern than the brief table that appeared in ICRP 23, and the average values could be used as future guidelines. Whole lung volumes have been determined from the voxel lung phantoms, and empirical formulae have been developed for males and females in this data set; these compare favorably with the published values in ICRP 66.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".