Monte Carlo dose calculations for phantoms with hip prostheses
Bibliographic record
Abstract
Computed tomography (CT) images of patients with hip prostheses are severely degraded by metal streaking artefacts. The low image quality makes organ contouring more difficult and can result in large dose calculation errors when Monte Carlo (MC) techniques are used. In this work, the extent of streaking artefacts produced by three common hip prosthesis materials (Ti-alloy, stainless steel, and Co-Cr-Mo alloy) was studied. The prostheses were tested in a hypothetical prostate treatment with five 18 MV photon beams. The dose distributions for unilateral and bilateral prosthesis phantoms were calculated with the EGSnrc/DOSXYZnrc MC code. This was done in three phantom geometries: in the exact geometry, in the original CT geometry, and in an artefact-corrected geometry. The artefact-corrected geometry was created using a modified filtered back-projection correction technique. It was found that unilateral prosthesis phantoms do not show large dose calculation errors, as long as the beams miss the artefact-affected volume. This is possible to achieve in the case of unilateral prosthesis phantoms (except for the Co-Cr-Mo prosthesis which gives a 3% error) but not in the case of bilateral prosthesis phantoms. The largest dose discrepancies were obtained for the bilateral Co-Cr-Mo hip prosthesis phantom, up to 11% in some voxels within the prostate. The artefact correction algorithm worked well for all phantoms and resulted in dose calculation errors below 2%. In conclusion, a MC treatment plan should include an artefact correction algorithm when treating patients with hip prostheses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".