Skeletal dosimetry in cone beam computed tomography
Bibliographic record
Abstract
Cone beam computed tomography (CBCT) is a relatively new patient imaging technique that has proved invaluable for treatment target verification and patient positioning during image-guided radiotherapy (IGRT). It has been shown that CBCT results in additional dose to bone that may amount to 10% of the prescribed dose. In this study, voxelized human phantoms, FAX06 (adult female) and MAX06 (adult male), are used together with phase-space data collected from a realistic model of a CBCT imager to calculate dose in the red bone marrow (RBM) and bone surface cells (BSCs), the two organs at risk within the bone spongiosa, during simulated head and neck, chest and pelvis CBCT scans. The FAX06/MAX06 phantoms model spongiosa based on micro-CT images, filling the relevant phantom voxels, which are 0.12 x 0.12 x 0.12 cm3, with 17 x 17 x 17 microm3 microvoxels to form a micromatrix of trabecular bone and bone marrow. FAX06/ MAX06 have already been implemented in an EGSnrc-based Monte Carlo code to simulate radiation transport in the phantoms; however, this study required significant modifications of the code to allow use of phase-space data from a simulated CBCT imager as a source and to allow scoring of total dose, RBM dose and BSC dose on a voxel-by-voxel basis. In simulated CBCT scans, the BSC dose is significantly greater than the dose to other organs at risk. For example, in a simulated head and neck scan, the average BSC dose is 25% higher than the average dose to eye lens (approximately 8.3 cGy), and 80% greater than the average dose to brain (5.7 cGy). Average dose to RBM, on the other hand, is typically only approximately 50% of the average BSC dose and less than the dose to other organs at risk (54% of the dose to eye lens and 76% of dose to brain in a head and neck scan). Thus, elevated dose in bone due to CBCT results in elevated BSC dose. This is potentially of concern when using CBCT in conjunction with radiotherapy treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".