Optimization of a table-top x-ray fluorescence computed tomography (XFCT) system
Bibliographic record
Abstract
Abstract Pencil beam x-ray fluorescence computed tomography (XFCT) has typically used a single spectrometer and prohibitively long scan times. However, detecting backscattered fluorescent x-rays from gold nanoparticles (AuNPs) using multiple spectrometers greatly reduces image noise and scan time. The arrangement of eight spectrometers for combined K-shell and L-shell XFCT was investigated along with a variety of conditions. A 2.5 cm-diameter cylindrical water phantom containing 4 mm-diameter vials with 0.1%–2% AuNP concentrations by weight was modeled by TOPAS, a GEANT4-based Monte Carlo software. The phantom was irradiated to 30 mGy by a 0.5 mm Pb-filtered 120 kVp and 1 mm Al-filtered 30 kVp 1 mm 2 x-ray pencil beam to yield respective Au K-shell and L-shell fluorescent x-rays, with 50 0.5 mm translation and 2-degree rotation steps. Eight CdTe and silicon drift detector (SDD) spectrometers were placed 2.25 cm away from the isocentre. The respective energy resolution was applied to the detected energy spectra and the spectra were corrected for detector response before extracting the fluorescence signal. Three CdTe and SDD spectrometer configurations (isotropic/backscattered grid/backscattered row arrangements), two CdTe crystal sizes (9 mm 2 /25 mm 2 ), two scanning techniques (moving/stationary spectrometers) and five vial-edge depths (0–4 mm) were considered in optimizing the contrast-to-noise ratio (CNR) for each XFCT image reconstructed with a maximum-likelihood expectation maximization (MLEM) algorithm. The isotropic spectrometer arrangement had AuNP detection sensitivities of 0.106% for K-shell and 0.132% for L-shell XFCT at 4 mm depth. Comparatively, the backscattered grid arrangement had the best AuNP sensitivity of 0.055% and 0.095%. The highest K-shell (0.044%) and L-shell (0.004%) AuNP sensitivities were found for vials at 0 mm depth. Using stationary spectrometers or the 9 mm 2 CdTe crystal compromised the CNR. For the best-case arrangement, L-shell XFCT is superior at vial-edge depths less than 3.0 mm. This work demonstrated the importance of spectrometer arrangement and vial depth for improving AuNP sensitivity and will guide the design for our table-top XFCT system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".