Multi-contrast CT imaging using a high energy resolution CdTe detector and a CZT photon-counting detector
Bibliographic record
Abstract
Abstract Computed tomography (CT) imaging with high energy resolution detectors shows great promise in material decomposition and multi-contrast imaging. Multi-contrast imaging was studied by imaging a phantom with iodine (I), gadolinium (Gd), and gold (Au) solutions, and mixtures of the three using a cadmium telluride (CdTe) spectrometer with an energy resolution of 1% as well as with a cadmium zinc telluride (CZT) detector with an energy resolution of 13%. The phantom was imaged at 120 kVp and 1.1 mA with 7 mm of aluminum filtration. For the CdTe data collection, the phantom was imaged using a 0.2 mm diameter x-ray beam with 96 ten-second data acquisitions across the phantom at 45 rotation angles. For the CZT detector, we had 720 projections using a cone beam, and the six detector energy thresholds were set to 23, 33, 50, 64, 81, and 120 keV so that three thresholds corresponded to the K-edges of the contrast agents. Contrast agent isolation methods were then examined. K-edge subtraction and novel spectrometric algebraic image reconstruction (SAIR) were used for the CdTe data. K-edge subtraction alone was used for the CZT data. Linearity plots produced similar R 2 values and slopes for all three reconstruction methods. Comparing CdTe methods, SAIR offered less noise than CdTe K-edge subtraction and better geometric accuracy at low contrast concentrations. CdTe contrast agent images of I, Gd, and Au offered less noise and greater contrast than the CZT images, highlighting the benefits of high energy resolution CdTe detectors for possible use in pre-clinical or clinical CT imaging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".