MO‐D‐144‐01: Ultrasound Guided RT Intervention & Novel Technologies
Bibliographic record
Abstract
Significant advances were made over the past decade in ultrasound (US) imaging and new therapeutic applications are emerging. Used alone or in combination with other imaging modalities, US imaging is also well suited for robot‐assisted interventions. This session will feature invited presentations on (1) the use of US and robot for interstitial breast implant high dose rate (HDR) brachytherapy, (2) the possibilities of electromagnetic tracking system for interactive needle navigation, 3D‐printed patient‐specific skewed‐needle template, and co‐robots for interventional brachytherapy, (3), the real time US guidance, dosimetry and treatment optimization for novel HDR brachytherapy such as single fraction partial prostate treatment for early stage disease, and (4) the development of an integrated 3D x‐ray/ultrasound imaging system for on‐board guidance of soft tissue targets for external beam radiation therapy. Learning Objectives: 1. Understand the improvements made on ultrasound imaging technology 2. Understand the possibilities of real‐time imaging for a wide range of applications 3. Identify new applications for medical physicists of ultrasound imaging for image‐guided interventions. Part of this work is supported by NCI R01 CA 161613, and another part by a research contract with Philips Medical Systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".