Film dosimetry studies for patient specific quality assurance in microbeam radiation therapy
Bibliographic record
Abstract
Microbeam radiation therapy (MRT) uses synchrotron arrays of X-ray microbeams to take advantage of the spatial fractionation effect for normal tissue sparing. In this study, radiochromic film dosimetry was performed for a treatment where MRT is introduced as a dose boost in a hypofractionated stereotactic radiotherapy (SRT) scheme. The isocenter dose was measured using an ionization chamber and two dimensional dose distributions were determined using radiochromic films. To compare the measured dose distribution to the MRT treatment plan, peak and valley were displayed in separate dosemaps. The measured and computed isocenter doses were compared and a two-dimensional 2%/2 mm normalized γ-index analysis with a 90% passing rate criterion was computed. For SRT, a difference of 2.6% was observed in the dose at the isocenter from the treatment plan and film measurement, with a passing rate of 96% for the γ-index analysis. For MRT, peak and valley doses differences of 25.6% and 8.2% were observed, respectively but passing rates of 96% and 90% respectively were obtained from the normalized γ-index maps. The differences in isocenter doses measured in MRT should be further investigated. We present the methodology of patient specific quality assurance (QA) for studying MRT dose distributions and discuss ideas to improve absolute dosimetry. This patient specific QA will be used for large animal trials quality assurance where MRT will be administered as a dose boost in conventional SRT. The observed remaining discrepancies should be studied against approximations in the TPS phantom materials, beams characteristics or film read-out procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".