Investigation of a novel 2.5 MV sintered diamond target beam for intracranial linac-based stereotactic treatments
Bibliographic record
Abstract
Abstract Purpose . This work investigates the small-field dosimetric characteristics of a 2.5 MV sintered diamond target beam and its feasibility for use in linac-based intracranial stereotactic treatments. Due to the increased proportion of low energy photons in the low-Z beam, it was hypothesized that this novel beam would provide sharper dose fall-off compared to the 6 MV beam owing to the reduced energy, and therefore range, of secondary electrons. Methods . Stereotactic treatments of ocular melanoma and trigeminal neuralgia were simulated for 2.5 MV low-Z and 6 MV beams using Monte Carlo to calculate dose in a voxelized anatomical phantom. Two collimation methods were investigated, including a 5 × 3 mm 2 HDMLC field and a 4 mm cone to demonstrate isolated and combined effects of geometric and radiological contributions to the penumbral width. Results . The measured 2.5 MV low-Z dosimetric profiles demonstrated reduced penumbra by 0.5 mm in both the inline and crossline directions across all depths for both collimation methods, compared to 6 MV. In both treatment cases, the 2.5 MV low-Z beam collimated with the 4 mm cone produced the sharpest dose fall off in profiles captured through isocenter. This improved fall-off resulted in a 59% decrease to the maximum brainstem dose in the trigeminal neuralgia case for the 2.5 MV low-Z MLC collimated beam compared to 6 MV. Reductions to the maximum and mean doses to ipsilateral and contralateral OARs in the ocular melanoma case were observed for the 2.5 MV low-Z beam compared to 6 MV with both collimation methods. Conclusions . While the low dose rate of this novel beam prohibits immediate clinical translation, the results of this study support the further development of this prototype beam to decrease toxicity in intracranial SRS treatments.
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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 | 0.000 |
| 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) | 0.002 | 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".