Beam generation and planar imaging at energies below 2.40 MeV with carbon and aluminum linear accelerator targets
Bibliographic record
Abstract
PURPOSE: Recent work has demonstrated improvement of image quality with low-Z linear accelerator targets and energies as low as 3.5 MV. In this paper, the authors lower the incident electron beam energy between 1.90 and 2.35 MeV and assess the improvement of megavoltage planar image quality with the use of carbon and aluminum linear accelerator targets. METHODS: The bending magnet shunt current was adjusted in a Varian linear accelerator to allow selection of mean electron energy between 1.90 and 2.35 MeV. Linac set points were altered to increase beam current to allow experimental imaging in a practical time frame. Electron energy was determined through comparison of measured and Monte Carlo modeled depth dose curves. Planar image CNR and spatial resolution measurements were performed to quantify the improvement of image quality. Magnitudes of improvement are explained with reference to Monte Carlo generated energy spectra. RESULTS: After modifications to the linac, beam current was increased by a factor greater than four and incident electron energy was determined to have an adjustable range from 1.90 MeV to 2.35 MeV. CNR of cortical bone was increased by a factor ranging from 6.2 to 7.4 and 3.7 to 4.3 for thin and thick phantoms, respectively, compared to a 6 MV therapeutic beam for both aluminum and carbon targets. Spatial resolution was degraded slightly, with a relative change of 3% and 10% at 0.20 lp∕mm and 0.40 lp∕mm, respectively, when reducing energy from 2.35 to 1.90 MV. The percentage of diagnostic x-rays for the beams examined here, ranges from 46% to 54%. CONCLUSION: It is possible to produce a large fraction of diagnostic energy x-rays by lowering the beam energy below 2.35 MV. By lowering the beam energy to 1.90 MV or 2.35 MV, CNR improves by factors ranging from 3.7 to 7.4 compared to a 6 MV therapy beam, with only a slight degradation of spatial resolution when lowering the energy from 2.35 MV to 1.90 MV.
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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".