Two-dimensional inverse planning and delivery for precision preclinical radiobiological investigations
Bibliographic record
Abstract
Advances in preclinical radiotherapy systems have provided the technical foundations for delivering highly heterogeneous dose distributions for unique radiobiological experiments, but methods to deliver arbitrary dose distributions are in their infancy. This study developed a method to optimize and automatically deliver planar dose distributions on a recently developed preclinical radiotherapy platform. The method was based on empirically determined dose kernel distributions from radiochromic film measurements. These kernels were used to determine optimal animal stage positions and beam weights to deliver a desired dose distribution at a given depth using a sequential quadratic programming optimization algorithm. The method was validated by end-to-end delivery of two dosimetric challenges designed to quantify targeting and dosimetric accuracy. The results revelead an overall targeting accuracy of 112 μm and a dosimetric delivery error, calculated along four line profiles in radiochromic film measurements, of 6.8%. Mean absolute delivery error across a linear dose gradient between 0 and 1 Gy over 7.5 mm was 0.03 Gy. These results confirm the optimization framework is an effective platform for delivery of millimetre scale heterogeneous dose distributions with sub-millimetre accuracy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".