Multi-contrast K-edge imaging on a bench-top photon-counting CT system: acquisition parameter study
Bibliographic record
Abstract
Purpose. Photon-counting computed tomography (PCCT) shows promise for medical imaging in regards to material separation and imaging of multiple contrast agents. However, many PCCT setups are under development and are not optimized for specific contrast agents or use cases. Here, we demonstrate how experimental system parameters may be varied in order to enhance performance and we propose a set of recommendations to achieve this based on contrast agent. Approach. A table-top PCCT system with a cadmium zinc telluride (CZT) detector capable of separating six energy bins was used to image multiple contrast agents in a small phantom. The contrast agents were separated and the concentration was quantified using K-edge subtraction. To increase system performance, we investigated three parameters: beam filter type and thickness, projection acquisition time, and energy bin width. The results from the parameters were compared based on PCCT signal and contrast to noise ratio (CNR) or noise in K-edge images. The concentrations of the contrast agents were quantified in K-edge images and compared to known concentrations. Results. The bench-top PCCT system was able to successfully quantify the contrast agents through K-edge subtraction. Decreasing projection acquisition time showed a decrease in K-edge CNR. However, it did not scale as the square root of time. Filter type and bin width demonstrated a dependence on the specific contrast agent. Conclusions. The presented bench-top system demonstrated the ability to separate contrast agents using K-edge subtraction and accurately determine contrast concentration in K-edge images. Specific parameters for future use will be chosen based on contrast agent.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".