Evaluation of New Commercially Available Metal Artifact Reduction (MAR) Algorithm on Both Image Quality and Relative Dosimetry for Patients with Hip Prosthesis or Dental Fillings
Bibliographic record
Abstract
Streaking artifacts on computed tomography (CT) images are caused by high density materials such as hip prosthesis, surgical clips and dental fillings. The artifacts can lead to compromised clinical outcome due to the inability to differentiate tumor volume and the uncertainties in dose calculation. The goals of our study are to evaluate how GE’s smart metal artifact reduction (MAR) algorithm impacts image quality on phantoms and dosimetry on head and neck patients with dental fillings and pelvic patients with hip prosthesis. Treatment plans calculated on the MAR and non-MAR datasets with the same beam arrangements and fluence are compared. Dose differences between the MAR and non-MAR datasets are not significant. However, substantial reductions of metal artifacts are observed when MAR algorithm is applied. Planning on the MAR dataset is recommended since it improves image quality and CT number accuracy. It also negates the need to contour the artifacts and override the density which can be time consuming.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".