MO‐SAMS‐AUD‐01: Daily Localization — I: Kilovolt Imaging
Bibliographic record
Abstract
As high‐precision 3‐D conformal radiation therapy and intensity‐modulated radiation therapy have become standard practice, radiographic imaging using kilovoltage (kV) x‐ray sources has been rapidly implemented for in‐room target localization and patient positioning to ensure conformal dose delivery. Various types of imaging devices are commercially available for clinical applications and their typical imaging functionalities include 2‐D radiographic and fluoroscopic imaging as well as 3‐D cone beam CT. There are substantial demands for fundamental understanding of what kind of systems can be used for clinical practice, when and what imaging systems should be used, how they can be properly used for daily target localization for different anatomical sites, and what kind of quality assurance programs are needed. In this session, we will briefly introduce the latest commercially available imaging systems using kV imaging for in‐room target localization and their imaging principles. Clinical applications and imaging protocols using these systems for accurate target localization and patient positioning will then be discussed. Finally, systematic quality assurance procedures will be presented. Objectives: 1. Understand the latest commercially available technologies for in‐room kV radiography, fluoroscopy, and cone‐beam CT and their basic imaging principles. 2. Understand the basic clinical imaging applications for daily localization. 3. Understand the basic system limitations and QA components of a comprehensive QA program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".