Investigating neutron activated contrast agent imaging for tumor localization in proton therapy: a feasibility study for proton neutron gamma-x detection (PNGXD)
Bibliographic record
Abstract
Abstract Proton neutron gamma-x detection (PNGXD) is a novel imaging concept being investigated for tumor localization during proton therapy that uses secondary neutron interactions with a gadolinium contrast agent (GDCA) to produce characteristic photons within the 40–200 keV energy region. The purpose of this study is to experimentally investigate the feasibility of implementing this procedure by performing experimental measurements on a passive double scattering proton treatment unit. Five experimental measurements were performed with varying concentrations and irradiation conditions. Photon spectra were measured with a 25 mm 2 , 1 mm thick uncollimated X-123 CdTe spectrometer. For a 10.4 Gy administration on a 100 ml volume phantom with 10 mg g −1 Gd solution placed in a water phantom, 1129 ± 184 K-shell Gd counts were detected. For an administered dose of 21 Gy and the same Gd solution measured in air, resulted in 3296 ± 256 counts. A total of 1094 ± 171, 421 ± 150 and 23 ± 141 K-shell Gd counts were measured for Gd concentrations of 10 mg g −1 , 1 mg g −1 and 0 mg g −1 for 7 Gy dose in air. The signal to noise ratio for these five measurements were: 7, 15, 6, 3, and 0.2, respectively. The spectrum contained 43 keV K α and 49 keV K β peaks, however a small amount of 79.5 and 181.9 keV prompt gamma rays were detected from gadolinium neutron capture. This discrepancy is due to a drop in the intrinsic detection efficiency of the CdTe spectrometer over this energy range. The measurements were compared with Monte-Carlo simulation to determine the contributions of Gd neutron capture from internal and external neutrons on a passive scattering proton therapy unit and to investigate the discrepancy in detected characteristic x-rays versus prompt gamma rays.
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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 | 0.001 |
| 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 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".