WE‐G‐18A‐07: Clinical Evaluation of Normalized Metal Artifact Reduction in KVCT Using MVCT Prior Images (MVCT‐NMAR) Technique in Radiotherapy
Bibliographic record
Abstract
Purpose: To evaluate the metal artifacts in diagnostic kVCT images of patients that are corrected using a normalized metal artifact reduction method with MVCT prior images, MVCT‐NMAR. Methods: An MVCTNMAR algorithm was developed and applied to five patients: three with bilateral hip prostheses, one with unilateral hip prosthesis and one with dental fillings. The corrected images were evaluated for visualization of tissue structures and their interfaces, and for radiotherapy dose calculations. They were also compared against the corresponding images corrected by a commercial metal artifact reduction technique, O‐MAR, on a Phillips™ CT scanner. Results: The use of MVCT images for correcting kVCT images in the MVCT‐NMAR technique greatly reduces metal artifacts, avoids secondary artifacts, and makes patient images more useful for correct dose calculation in radiotherapy. These improvements are significant over the commercial correction method, provided the MVCT and kVCT images are correctly registered. The remaining and the secondary artifacts (soft tissue blurring, eroded bones, false bones or air pockets, CT number cupping within the metal) present in O‐MAR corrected images are removed in the MVCT‐NMAR corrected images. Large dose reduction is possible outside the planning target volume (e.g., 59.2 Gy in comparison to 52.5 Gy in pubic bone) when these MVCT‐NMAR corrected images are used in TomoTherapy™ treatment plans, as the corrected images no longer require directional blocks for prostate plans in order to avoid the image artifact regions. Conclusion: The use of MVCT‐NMAR corrected images in radiotherapy treatment planning could improve the treatment plan quality for cancer patients with metallic implants. Moti Raj Paudel is supported by the Vanier Canada Graduate Scholarship, the Endowed Graduate Scholarship in Oncology and the Dissertation Fellowship at the University of Alberta. The authors acknowledge the CIHR operating grant number MOP 53254.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".