On the use of an analytic source model for dose calculations in precision image-guided small animal radiotherapy
Bibliographic record
Abstract
Precision image-guided small animal radiotherapy is rapidly advancing through the use of dedicated micro-irradiation devices. However, precise modeling of these devices in model-based dose-calculation algorithms such as Monte Carlo (MC) simulations continue to present challenges due to a combination of very small beams, low mechanical tolerances on beam collimation, positioning and long calculation times. The specific intent of this investigation is to introduce and demonstrate the viability of a fast analytical source model (AM) for use in either investigating improvements in collimator design or for use in faster dose calculations. MC models using BEAMnrc were developed for circular and square fields sizes from 1 to 25 mm in diameter (or side) that incorporated the intensity distribution of the focal spot modeled after an experimental pinhole image. These MC models were used to generate phase space files (PSFMC) at the exit of the collimators. An AM was developed that included the intensity distribution of the focal spot, a pre-calculated x-ray spectrum, and the collimator-specific entrance and exit apertures. The AM was used to generate photon fluence intensity distributions (ΦAM) and PSFAM containing photons radiating at angles according to the focal spot intensity distribution. MC dose calculations using DOSXYZnrc in a water and mouse phantom differing only by source used (PSFMC versus PSFAM) were found to agree within 7% and 4% for the smallest 1 and 2 mm collimator, respectively, and within 1% for all other field sizes based on depth dose profiles. PSF generation times were approximately 1200 times faster for the smallest beam and 19 times faster for the largest beam. The influence of the focal spot intensity distribution on output and on beam shape was quantified and found to play a significant role in calculated dose distributions. Beam profile differences due to collimator alignment were found in both small and large collimators sensitive to shifts of 1 mm with respect to the central axis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".