Generation and modelling of megavoltage photon beams for contrast-enhanced radiation therapy
Bibliographic record
Abstract
Contrast-enhanced radiation therapy (CERT) is a treatment approach involving the irradiation of tumours containing high atomic number (Z) contrast media, using low-quality x-ray beams. This work describes the experimental generation of x-ray beams using a linear accelerator with low-Z target materials (beryllium and aluminium), in order to produce photon energy spectra appropriate for CERT. Measurements were made to compare the experimental beams to conventional linear accelerator photon beams in terms of per cent depth dose. Monte Carlo simulation was used to model the generation of each beam, and models were validated against experimental measurement. Validated models were used to demonstrate changes in photon spectra as well as to quantify the variation of tumour dose enhancement with iodinated contrast medium concentration in a simulated tumour volume. Finally, the ratio of the linear attenuation coefficient for iodinated contrast medium relative to water was determined experimentally as a function of iodine concentration. Beams created with low-Z targets show significant changes in energy spectra compared to conventional beams. For the 4 MeV/Be beam, for example, 33% of photons have energies below 60 keV. Measurements and calculation show that both the linear attenuation coefficient ratio and dose enhancement factor (DEF) increase most rapidly at concentrations below 46 mg I ml(-1). There is a significant dependence of DEF on electron energy and a lesser dependence on target material. The 4 MeV/Be beam is the most promising in terms of magnitude of DEF - for example, DEF values of 1.16 and 1.29 are obtained for concentrations of 20 mg I ml(-1) and 50 mg I ml(-1), respectively. DEF will increase or decrease, respectively, for shallower or deeper tumours at a rate of approximately 1.1% cm(-1). In summary, we show that significant dose enhancement is possible by altering the linear accelerator target and filtration, but the magnitude is highly dependent on contrast medium concentration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".