Absorbed and effective dose estimates in HR-pQCT of the distal radius and tibia: virtual dosimetry with a GPU-accelerated Monte Carlo code
Bibliographic record
Abstract
Abstract Objective . To obtain maps of absorbed dose and an estimate of the effective dose to an adult patient for a high resolution peripheral quantitative computed tomography (HR-pQCT) (XtremeCT II) examination of the distal tibia and radius, using a graphical processing unit (GPU)-based Monte Carlo (MC) code. Approach . We adapted the validated code gCTD (GPU-based CT Dose calculator), to replicate the HR-pQCT configuration. MC simulations were performed on digital phantoms of the tibia and radius obtained from bilateral scans of the ankle and wrist of a 25 year-old female volunteer. Scans were segmented using an ad hoc algorithm in Fiji. Simulations run on an NVIDIA GeForce RTX 3090 GPU board. MC dose estimates were validated via computed tomography dose index measurements. Main Results. We obtained the absorbed dose distribution in the skin, bone, bone marrow, fat, and muscle tissues. The effective dose for the HR-pQCT examination were 2.05 μ Sv and 2.13 μ Sv for the right and left tibia, and 1.48 μ Sv and 1.49 μ Sv for the right and left radius, respectively, with a Type A statistical uncertainty of 0.06% ( k = 3) with 4.66 × 10 11 photon histories. Corresponding effective dose conversion coefficients ( k-factors ) were 0.185 μ Sv ∙ mGy −1 · cm −1 (tibia), and 0.133 μ Sv mGy −1 · cm −1 (radius). Significance. We reported the first independent estimate of the effective dose for standard HR-pQCT clinical scans of the distal tibia and radius with the XtremeCT II scanner. Effective dose estimates (considering a total relative uncertainty of less than 40%) were lower than those indicated by the manufacturer and commonly reported for these scans. With 4.66 × 10 9 photon histories, the gCTD MC code can produce 3D dose maps from segmented HR-pQCT images in less than 12 s (GPU time), with 0.9% ( k = 3) statistical uncertainty, making real-time personalized dose estimate feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".