ASSESSMENT OF THE CHEST WALL THICKNESS OF THE LAWRENCE LIVERMORE TORSO PHANTOM USING A VOXEL IMAGE
Bibliographic record
Abstract
This paper describes the methodology of measuring the chest wall thickness using the voxel image of the Lawrence Livermore National Lab (LLNL) torso phantom. The LLNL phantom is used as a standard to calibrate a lung counter consisting of a 2 × 2 array of germanium detectors. In general, an average thickness estimated from four counting positions is used as the chest wall thickness for a given overlay plate. For a given overlay, the outer chest surface differs from that of inner one, and the chest wall thickness varies from one position to other. The LLNL phantom with chest plate and C4 overlay plate installed was scanned with a CT (computed tomography) scanner. The image data, collected in DICOM (Digital Imaging and Communication) format, were converted to the MCNP input file by using the Scan2Mcnp program. The MCNP file was visualized and analyzed with the Moritz visual editor. An analytic expression was formulated and solved to calculate the chest wall thickness by using the point detector responses (F 5 tally of MCNP). To map the chest thickness, the entire chest wall was meshed into virtual grids of 1 cm width. A source and detector pair was moved along the inner and outer surface of the chest wall from right to left at different heights from neck to abdomen. For each height (z(k)), (x(i), y(j)) coordinates for the detector source pair were calculated from the visual editor and were scaled on-screen. For each (x(i), y(j), z(k)) position, a mesh thickness was measured from on-screen measurement and by solving the detector responses. The chest wall thicknesses at different positions on the outer surface of the chest were compared and verified using two methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".