AAPM Task Group 241: A medical physicist’s guide to MRI‐guided focused ultrasound body systems
Bibliographic record
Abstract
Magnetic resonance-guided focused ultrasound (MRgFUS) is a completely non-invasive technology that has been approved by FDA to treat several diseases. This report, prepared by the American Association of Physicist in Medicine (AAPM) Task Group 241, provides background on MRgFUS technology with a focus on clinical body MRgFUS systems. The report addresses the issues of interest to the medical physics community, specific to the body MRgFUS system configuration, and provides recommendations on how to successfully implement and maintain a clinical MRgFUS program. The following sections describe the key features of typical MRgFUS systems and clinical workflow and provide key points and best practices for the medical physicist. Commonly used terms, metrics and physics are defined and sources of uncertainty that affect MRgFUS procedures are described. Finally, safety and quality assurance procedures are explained, the recommended role of the medical physicist in MRgFUS procedures is described, and regulatory requirements for planning clinical trials are detailed. Although this report is limited in scope to clinical body MRgFUS systems that are approved or currently undergoing clinical trials in the United States, much of the material presented is also applicable to systems designed for other applications.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.036 | 0.070 |
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".