Poster - Thurs Eve-18: Performance evaluation of MV CT imaging on the HI ART II tomotherapy unit
Bibliographic record
Abstract
The HI-ART II unit (TomoTherapy Inc., Madison WI) is a modality used by the London Regional Cancer Program (LRCP) for radiation therapy. This machine uses the same source of Megavoltage energy radiation to image (3.5 MV) and to treat (6MV) patients, combining the functionality of a traditional linear accelerator and CT simulator into one unit. Thus, it is possible to assess patient positioning and adjust for anatomy changes just prior to radiation therapy. Unfortunately, at MV energy levels, the physics of radiation interaction limits image quality, and gives rise to an inherent dose limitation concern that enhances noise levels. Therefore, we propose to quantify the image quality produced by the HI-ART II unit using techniques established for kVCT scanner technology. Our study involved the use of three standard phantoms to test image resolution, noise, uniformity, and linearity for a 512 × 512 reconstruction matrix and three scan pitch settings (0.8, 1.6, and 2.4). Results follow: linearity between MV CT number versus relative electron density was observed, noise calculations ranged from 2.15-2.51%, and a distinct central artifact was revealed during uniformity testing. The linearity between MV CT number versus relative electron density implies that MV CT images are highly suitable for dose calculations. MV CT image quality of uniform phantoms were acceptable and demonstrated noise levels higher than those produced by kVCT simulators. Further study is necessary to correct for the central artifact in MV CT images.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".