Photon-counting computed tomography of lanthanide contrast agents with a high-flux 330-μm-pitch cadmium zinc telluride detector in a table-top system
Bibliographic record
Abstract
Purpose: We present photon-counting computed tomography (PCCT) imaging of contrast agent triplets similar in atomic number (Z) achieved with a high-flux cadmium zinc telluride (CZT) detector. Approach: The table-top PCCT imaging system included a 330-μm-pitch CZT detector of size 8 mm × 24 mm2 capable of using six energy bins. Four 3D-printed 3-cm-diameter phantoms each contained seven 6-mm-diameter vials with water and low and high concentration solutions of various contrast agents. Lanthanum (Z = 57), gadolinium (Gd) (Z = 64), and lutetium (Z = 71) were imaged together and so were iodine (Z = 53), Gd, and holmium (Z = 67). Each phantom was imaged with 1-mm aluminum-filtered 120-kVp cone beam x rays to produce six energy-binned computed tomography (CT) images. Results:K-edge images were reconstructed using a weighted sum of six CT images, which distinguished each contrast agent with a root-mean-square error (RMSE) of <0.29 % and 0.51% for the 0.5% and 5% concentrations, respectively. Minimal cross-contamination in each K-edge image was seen, with RMSE values <0.27 % in vials with no contrast. Conclusion: This is the first preliminary demonstration of simultaneously imaging three similar Z contrast agents with a difference in Z as low as 3.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".