Photon-counting detector step-wedge calibration enabling water and iodine material decomposition
Bibliographic record
Abstract
Abstract Photon-counting detector computed tomography (PCD-CT) has demonstrated improvements in conventional image quality compared to energy integrating detector (EID) CT. PCD-CT has the advantage of being able to operate in conventional and spectral mode simultaneously by sorting photons according to selected energy thresholds. However, to reconstruct spectral images PCD-CT requires extensive calibration and specifically fine-tuning a spectral response. This response is then used to perform material decomposition (MD). We propose a step-wedge phantom made of water and iodine to calibrate a prototype PCD-CT system. Four methods were tested and compared based on calibration accuracy and CT image quality. The exhaustive PCD response was not well calibrated, but a reduced model was defined that was able to perform accurate water/iodine MD and to reduce the ring artifact intensity. The impact of the number of energy bins (from two to seven) was also studied. The number of bins did not affect the spectral accuracy. However, compared to the two energy bin configuration, the seven bin configuration decreased the noise by 10% and 15% in the water and iodine maps, respectively. The model was tested on ex-vivo tissue samples injected with iodine to demonstrate the results of the water/iodine MD on biological materials.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".