Image guided IMRT dosimetry using anatomy specific MOSFET configurations
Bibliographic record
Abstract
We have investigated the feasibility of using a set of multiple MOSFETs in conjunction with the mobile MOSFET wireless dosimetry system, to perform a comprehensive and efficient quality assurance (QA) of IMRT plans. Anatomy specific MOSFET configurations incorporating 5 MOSFETs have been developed for a specially designed IMRT dosimetry phantom. Kilovoltage cone beam computed tomography (kV CBCT) imaging was used to increase the positional precision and accuracy of the detectors and phantom, and so minimize dosimetric uncertainties in high dose gradient regions. The effectiveness of the MOSFET based dose measurements was evaluated by comparing the corresponding doses measured by an ion chamber. For 20 head and neck IMRT plans the agreement between the MOSFET and ionization chamber dose measurements was found to be within -0.26 +/- 0.88% and 0.06 +/- 1.94% (1 sigma) for measurement points in the high dose and low dose respectively. A precision of 1 mm in detector positioning was achieved by using the X-Ray Volume Imaging (XVI) kV CBCT system available with the Elekta Synergy Linear Accelerator. Using the anatomy specific MOSFET configurations, simultaneous measurements were made at five strategically located points covering high dose and low dose regions. The agreement between measurements and calculated doses by the treatment planning system for head and neck and prostate IMRT plans was found to be within 0.47 +/- 2.45%. The results indicate that a cylindrical phantom incorporating multiple MOSFET detectors arranged in an anatomy specific configuration, in conjunction with image guidance, can be utilized to perform a comprehensive and efficient quality assurance of IMRT plans.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".