Neutron activation of gadolinium for ion therapy: a Monte Carlo study of charged particle beams
Bibliographic record
Abstract
Abstract This study investigates the photon production from thermal neutron capture in a gadolinium (Gd) infused tumor as a result of secondary neutrons from particle therapy. Gadolinium contrast agents used in MRI are distributed within the tumor volume and can act as neutron capture agents. As a result of particle therapy, secondary neutrons are produced and absorbed by Gd in the tumor providing potential enhanced localized dose in addition to a signature photon spectrum that can be used to produce an image of the Gd enriched tumor. To investigate this imaging application, Monte Carlo (MC) simulations were performed for 10 different particles using a 5–10 cm spread out-Bragg peak (SOBP) centered on an 8 cm 3 , 3 mg/g Gd infused tumor. For a proton beam, 1.9 × 10 6 neutron captures per RBE weighted Gray Equivalent dose (GyE) occurred within the Gd tumor region. Antiprotons ( $$\bar{P}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> </mml:math> ), negative pions (− π), and helium (He) ion beams resulted in 10, 17 and 1.3 times larger Gd neutron captures per GyE than protons, respectively. Therefore, the characteristic photon based spectroscopic imaging and secondary Gd dose enhancement could be viable and likely beneficial for these three particles.
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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 | 0.001 | 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".