IMRT point dose measurements with a diamond detector
Bibliographic record
Abstract
Background. Radiation dose distribution calculations used in treatment planning systems (TPS) describe dose deposition well for large fields. For small fields encountered in Intensity Modulated Radiation Therapy (IMRT) these models may be less accurate. Dose verification of IMRT fields is therefore essential in IMRT implementation and quality assurance. For these smaller fields, lateral electronic equilibrium may not exist and volume averaging effects in ion chambers become increasingly problematic. For this reason, detectors with sensitive volumes smaller than that of conventional ion chambers are preferable in both small fields and high dose gradient region. Diamond detectors are capable of making such accurate dosimetric measurements. Methods. This study compares dosimetry measurements made with a PTW-Freiburg type 60003 diamond detector, an Exradin A12 ion chamber, a PTW-Freiburg PinPoint ion chamber and a Varian aS500 EPID. Dose measurements were made in a clinical prostate intensity modulated beam. Due to difficulties encountered when dosimetric measurements are made in high dose gradient regions, probe positioning within IMRT fields was investigated and a method to establish better probe positions is proposed. Measured doses were compared with HELAX-TMS calculated doses to verify performance of the TPS used in this center. Results. The diamond detector dosimetry is extremely sensitive to positioning particularly in high dose gradient regions. The results indicate that improved agreement between doses measured with various dosimeters can be obtained by appropriate selection of the probe position. Avoidance of high dose gradient regions improves agreement between measured doses particularly for the PinPoint chamber, the diamond detector and the EPID. Conclusions. The use of diamond detectors and EPIDs in dosimetry is an attractive option particularly for verification of IMRT treatments. Although 2D dose verification of IMRT treatments is a more desirable option than point dose verification, an independent check of EPID or film verification is beneficial. Use of a diamond detector is an excellent option for dose measurements in cases where portal imaging devices are not available such as the case of helical tomotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".