Design of a combined X-ray fluorescence Computed Tomography (CT) and photon-counting CT table-top imaging system
Bibliographic record
Abstract
This paper demonstrates the capability of a table-top x-ray imaging system to simultaneously image a small animal phantom loaded with Gd and Au contrast agents with photon-counting computed tomography (PCCT) and x-ray fluorescence CT (XFCT). Our table-top system consisted of a diagnostic x-ray tube, translation and rotation stages, a 330 μm-pitch cadmium zinc telluride (CZT) photon-counting detector with six energy bins, and two CdTe spectrometers which detected fluorescent x-rays in 0.5 keV bins. The energy bin thresholds on the CZT detector matched the contrast agent K-edge energies, enabling K-edge PCCT. A 3 cm-diameter phantom containing vials of 1% and 5% Gd and Au solutions by weight, including a mixed 1% Gd/Au vial, was scanned with 0.5 mm Cu-filtered 120 kVp x-rays at 2 mA. A pencil beam geometry was used with 33 translation and 30 rotation steps to form XFCT and K-edge PCCT images. A cone beam geometry with 180 rotation steps was used to form higher-resolution K-edge PCCT images. Three mice were each injected post-mortem with 0.2 mL of 5% Gd, Au, or mixed Gd/Au solutions in the upper torso and imaged similarly to the phantom. The lowest detectable concentration based on the Rose criterion for Gd and Au phantom data was 0.75% and 3.0% for XFCT, 0.81% and 0.82% for pencil beam K-edge PCCT, and 0.79% and 1.5% for cone beam K-edge PCCT. However, cone beam K-edge PCCT images have a much higher spatial resolution than the pencil beam images. The mixed 1% Gd/Au signal was diminished in the K-edge PCCT phantom images. Preliminary mouse images of Gd and Au among the imaging techniques were similar in image quality as the phantom images. This is the first demonstration of simultaneously-acquired XFCT and PCCT of both Gd and Au in a phantom on a table-top imaging system. On our system, cone beam PCCT is best for imaging Gd, pencil beam PCCT is best for imaging Au, and pencil beam XFCT is better suited toward resolving mixtures of contrast agents.
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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 | 0.001 |
| 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".