Megavoltage image contrast with low-atomic number target materials and amorphous silicon electronic portal imagers
Bibliographic record
Abstract
Low-atomic number (Z) targets have been shown to improve contrast in megavoltage (MV) images when using film-screen detection systems. This research aims to quantify the effect of low-Z targets on MV image contrast using an amorphous silicon electronic portal image detector (a-Si EPID) through both experimental measurement and Monte Carlo (MC) simulation. Experimental beams were produced with the linac running in the 6 MeV electron mode and with a 1.0 cm aluminum (Al, Z = 13) target replacing flattening filtration in the carousel, (6 MeV/Al). A 2100EX Varian linac equipped with an aS500 EPID was used with the QC3 MV phantom for the majority of contrast measurements. The BEAMnrc/EGSnrc MC package was used to build a model of the full imaging system including beam generation (linac head), the a-Si detector and the contrast phantom. The model accurately reproduces contrast measurements to within 2.5% for both the standard 6 MV therapy beam and the 6 MeV/Al beam. The contrast advantage of 6 MeV/Al over 6 MV, as quantified with the QC3 phantom, ranged from a factor increase of 1.6 +/- 0.1 to 2.8 +/- 0.2. Only a modest improvement in contrast was seen when the incident electron energy was reduced to 4 MeV (up to factor of 1.2 +/- 0.1 over 6 MeV/Al) or with removal of the copper build-up layer in the detector, (up to factor of 1.2 +/- 0.1 over 6 MeV/Al). Further decreasing the target Z, to beryllium (Be, Z = 4), at 4 MeV showed no significant improvement over 4 MeV/Al. Experimentally, the contrast advantage of 6 MeV/Al over 6 MV was found to decrease with increasing patient thickness, as can be expected due to selective attenuation of low-energy photons. At head and neck-like thicknesses, the low-Z advantage is a factor increase of 1.7 +/- 0.1.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".