Poster — Thur Eve — 11: Validation of the orthopedic metallic artifact reduction tool for CT simulations at the Ottawa Hospital Cancer Centre
Bibliographic record
Abstract
Metallic implants in patients can produce image artifacts in kilovoltage CT simulation images which can introduce noise and inaccuracies in CT number, affecting anatomical segmentation and dose distributions. The commercial orthopedic metal artifact reduction algorithm (O‐MAR) (Philips Healthcare System) was recently made available on CT simulation scanners at our institution. This study validated the clinical use of O‐MAR by investigating its effects on CT number and dose distributions. O‐MAR corrected and uncorrected images were acquired with a Philips Brilliance Big Bore CT simulator of a cylindrical solid water phantom that contained various plugs (including metal) of known density. CT number accuracy was investigated by determining the mean and standard deviation in regions of interest (ROI) within each plug for uncorrected and O‐MAR corrected images and comparing with no‐metal image values. Dose distributions were calculated using the Monaco treatment planning system. Seven open fields were equally spaced about the phantom around a ROI near the center of the phantom. These were compared to a “correct” dose distribution calculated by overriding electron densities a no‐metal phantom image to produce an image containing metal but no artifacts. An overall improvement in CT number and dose distribution accuracy was achieved by applying the O‐MAR correction. Mean CT numbers and standard deviations were found to be generally improved. Exceptions included lung equivalent media, which is consistent with vendor specified contraindications. Dose profiles were found to vary by ±4% between uncorrected or O‐MAR corrected images with O‐MAR producing doses closer to ground truth.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".