3D Printed Lung Phantom for Individual Monitoring
Bibliographic record
Abstract
ABSTRACT: The Human Monitoring Laboratory, Health Canada (HML), has used a 3D printer to re-engineer its Lawrence Livermore National Laboratory (LLNL) foam lung sets (manufactured by Radiology Support Devices, Inc., Long Beach, CA). The foam sets are currently the HML standard for calibrating and performance testing lung-counting systems in Canada. This paper describes the process of creating and validating new 3D-printed lung sets modeled from one of the HML's existing RSD foam sets. The existing sets were custom made, making them costly and difficult to obtain or replace. Also, after many years of use, the HML has found that they are prone to wear and tear. When used with planar inserts containing various isotopes, the blank sets can become contaminated and are difficult to clean. Using 3D printing, the HML has created new blank lung sets that are nearly identical copies of the originals and are inexpensive and easily manufactured. Measurements using natural uranium (Nat U), 241Am, and 152Eu planar lung inserts were performed to compare obtained efficiencies at a wide range of energies using the original RSD foam sets and the 3D-printed ones. Both the foam and the 3D-printed lung sets were counted using the LLNL chest phantom positioned in the same counting geometry in the lung counting system. Biases, all below 15%, were obtained between the foam and the 3D-printed sets for energies above 40 KeV. Based on these results, as well as cost benefits and ease of use, the HML has decided to replace its original RSD foam lung set with the 3D-printed version for its lung performance testing program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".