WE‐E‐332‐04: Skeletal Dosimetry in Cone Beam Computed Tomography
Bibliographic record
Abstract
Purpose: In a recent publication, Ding et al [Med. Phys. 35, (2008)1135] demonstrated that the dose in patient bony anatomy during a typical cone beam computed tomography (CBCT) scan is up to 3–4 times higher than that in soft tissues. The purpose of this investigation is to determine the dose to red bone marrow (RBM) and bone surface cells (BSC), identified by the ICRP as the two organs at risk in the skeleton. Method and Materials: The FAX06/MAX06 EGSnrc‐based code provides the ability to compute whole organ doses, including BSC and RBM doses, in a voxelized representation of a female/male body including micro‐structural information for the spongiosa obtained from micro‐CT images. The code is modified to permit the computation of spatial dose distributions and to allow the use of phase space files from BEAMnrc simulations to be employed as sources. A typical head‐and‐neck CBCT scan from a Varian Trilogy linac is investigated. Results: The average RBM dose in the FAX06 phantom is found to be 4.6 cGy, i.e., about 30% lower then the average dose in soft tissues such as the brain (6 cGy) and the eye lens (6.4 cGy), although a small fraction of the bone marrow (7%) receives doses in excess of 10 cGy. Due to the close BSC proximity to trabecular bone, the average BSC dose (11.2 cGy) is about 80% higher than the dose to soft tissue. The dose in about 15% of the BSC volume exceeds 15 cGy. Conclusion: The dose delivered to BSC and a small fraction of the RBM in typical head‐and‐neck CBCT scans is significantly higher than the dose in soft tissues. Repeated CBCT scans may therefore increase the risk of bone malformation and may cause growth issues in pediatric radiotherapy patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".