Skeletal dosimetry in the MAX06 and the FAX06 phantoms for external exposure to photons based on vertebral 3D-microCT images
Bibliographic record
Abstract
3D-microCT images of vertebral bodies from three different individuals have been segmented into trabecular bone, bone marrow and bone surface cells (BSC), and then introduced into the spongiosa voxels of the MAX06 and the FAX06 phantoms, in order to calculate the equivalent dose to the red bone marrow (RBM) and the BSC in the marrow cavities of trabecular bone with the EGSnrc Monte Carlo code from whole-body exposure to external photon radiation. The MAX06 and the FAX06 phantoms consist of about 150 million 1.2 mm cubic voxels each, a part of which are spongiosa voxels surrounded by cortical bone. In order to use the segmented 3D-microCT images for skeletal dosimetry, spongiosa voxels in the MAX06 and the FAX06 phantom were replaced at runtime by so-called micro matrices representing segmented trabecular bone, marrow and BSC in 17.65, 30 and 60 microm cubic voxels. The 3D-microCT image-based RBM and BSC equivalent doses for external exposure to photons presented here for the first time for complete human skeletons are in agreement with the results calculated with the three correction factor method and the fluence-to-dose response functions for the same phantoms taking into account the conceptual differences between the different methods. Additionally the microCT image-based results have been compared with corresponding data from earlier studies for other human phantoms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".