TH‐A‐213‐02: IAEA/AAPM Code of Practice for the Dosimetry of Static Small Photon Fields
Bibliographic record
Abstract
Modern radiotherapy techniques such as SRS/SRT, SBRT, IMRT, VMAT as well as specialized machines such as Tomotherapy, CyberKnife and Gamma Knife use small photon fields with at least one dimension <3 cm. Dosimetry in such small fields is challenging because of large detector perturbations due to non‐equilibrium conditions, occlusion of the primary photon source and large dose gradients across the field. Many small‐size dosimeters have been proposed for use in small fields. However, their characteristics especially the volume averaging and fluence perturbations have only recently been adequately understood. An IAEA‐AAPM working group has provided a framework for reference dosimetry in non‐compliant beams and the measurement of field output factors small fields (1). The AAPM TG‐155 (2) has adopted this framework to provide guidelines on relative dosimetry. This course explains the code of practice for absolute dosimetry that is under review and discusses the availability of correction factors to convert detector readings to doses. TG‐155 defines small field conditions, provides recommendations for suitable detectors and recommendations for good working practice for relative dosimetry (PDD, TMR, output factor, etc.) and dose calculations based on the new formulation. It also discusses beam modeling and dose calculations as a critical step in clinical utilization of small field radiotherapy. Alfonso et al, Med Phys 35, 5179–5186 (2008). Das et al, Med Phys (under review, 2014) Learning Objectives: Concepts and recommended procedures in the IAEA‐AAPM code of practice for dosimetry of small fields. Physics of dosimetry in non‐equilibrium conditions and definition of small fields Recommended procedures for relative dosimetry in TG‐1554. Choice of detectors for small field dosimetry, perturbations and corrections for dosimetry Understand the detector properties required for small field measurements Understand the advantages and limitations of commercially available detectors for small field dosimetry Research supported with operating grants from CIHR, NSERC, and BIOWin, a program funded at the Universite Catholique Louvain by the Walloon Government (Belgium). Student stipends supported by Medical Physics Research Training Network funded by the Collaborative Research and Training Experience program of the NSERC. Seuntjens: support by Sun Nuclear Corporation
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.054 |
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".