X-Ray Fluorescence Computed Tomography Induced by Photon, Electron, and Proton Beams
Bibliographic record
Abstract
X-ray fluorescence CT (XFCT) has shown promise for molecular imaging of gold nanoparticles. To date, XFCT has been induced by kilovoltage photon beams due to the high photoelectric interaction probability. We compare K-shell and L-shell XFCT induced by photon, electron, and proton beams for two phantom sizes. A 2.5 and 5.0-cm diameter phantom with four 5 mm and 10 mm vials, respectively, with gold-solutions of 0.1%-2% by weight were built in TOPAS, a GEANT4-based Monte Carlo simulation tool. The 2.5-cm phantom was imaged with XFCT induced by beams of 7.45 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> 81 keVand 5 MeV-photons, 220 kVpand 6 MV-photons, 10 MeVand 100 MeV-electrons, and 100 MeVand 250 MeVprotons. The doses between each phantom size were equal. First-generation CT geometry with 0.5mm × 0.5mm pencil beams with 0.5 mm-translation and 2°-rotation steps over each phantom was modeled. The scattered x-rays were detected on an idealized spherical detector from which the K-shell and L-shell fluorescent x-rays were extracted in 0.5 keV and 0.2 keV bins. XFCT images were generated using iterative reconstruction algorithms. The highest gold sensitivity was seen in the 81 keV-photon K-shell and L-shell images (0.004% and 0.007%) of the 5.0 cm-phantom at 30 mGy. For the 2.5 cm-phantom, the detection limits were 0.006%, 0.62%, and 0.28% for 81 keV-photon K-shell, 100 MeV-electron K-shell, and 100 MeV-proton L-shell images, respectively. The mean imaging dose was approximately 2-3 orders of magnitude higher in electron- and proton-XFCT compared to 81keV-photon XFCT. Our MC study demonstrates that the small-object XFCT imaging achieves the best performance when induced with kilovoltage-photon beams. Due to high imaging doses, electron- and proton-induced XFCT might be feasible for guiding nanoparticle-enhanced charged-particle radiotherapy.
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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 | 0.001 |
| 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".