Sci‐Sat AM (2) Therapy‐03: Characterization of Intermediate Energy X‐Ray Photons (0.2–1.0 MeV) for stereotactic radiosurgery: experimental demonstration of reduced radiological penumbra
Bibliographic record
Abstract
Introduction: Stereotactic radiosurgery is used to treat intracranial lesions with a high degree of accuracy. At the present time, x‐ray energies at or above Co‐60 gamma rays are used. We hypothesize that intermediate energy x‐ray photons (IEP's), combined with small field sizes, produce a reduced radiological penumbra leading to a sharper dose gradient. The purpose of this work is three‐fold: 1.) to produce IEP's using a medical linear accelerator 2.) to characterize the x‐ray energy 3.) to demonstrate reduced radiological penumbra for IEP's compared to 6MV x‐rays. Materials & Methods: A Siemens linear accelerator was modified to produce IEP's. PDD versus depth measurements were done in solid water using a Markus parallel plate ionization chamber (PTW Freiburg) at SSD=100cm for a 2×2cm2 field size. Monte Carlo computer simulations were done using MCNP‐4C. A penumbra measurement device was constructed to examine radiological penumbra for various photon energies, field sizes and depths. Film (Gafchromic EBT) was used to record field edge profiles. These films were scanned using a digital microscope (spatial resolution of 1.87 microns/pixel). Film irradiations were done using SSD=100cm, depth= 2cm, FS=1.1cm2. Results: For the IEP's, PDD values were 55.4%(surface), 62.6%(5cm), and 34.7%(10cm). Monte Carlo simulations suggest a nominal x‐ray energy of 800kV. The 80%–20% penumbra widths were 2.10mm (6MV) and 0.345mm (IEP's). Conclusions: A novel intermediate energy x‐ray beam (800kV) has been produced using a linear accelerator. There is more than a 5‐fold reduction in radiological penumbra for the IEP's versus 6MV x‐rays for the small field size examined.
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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 | 0.001 |
| 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) | 0.016 | 0.002 |
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".