Photon-counting detector spectral calibration enabling iodine quantification for spectral CT
Bibliographic record
Abstract
Photon-counting detector (PCD) computed tomography (CT) can provide grayscale and spectral images within the same acquisition. To achieve accurate material quantification, PCD-CT requires a pixel-per-pixel calibration. In this work, a novel step-wedge phantom made of various iodine concentrations and water thicknesses was used for such calibration. The phantom allowed for water/iodine material decomposition (MD) and so for a direct quantification of iodine concentration, relevant for CT angiography protocols. The general methodology refines a simulated forward model from measurements of known material thicknesses. However, this technique requires an accurate PCD response model which is difficult to simulate. Here we present a case where this exhaustive methodology led to an inaccurate MD. We tested two alternative models with a reduced number of parameters and a polynomial approach, for spectral accuracy comparison. The reduced parameter methods were compared with real data acquired with a CdZnTe (CZT) PCD with six energy thresholds. The different approaches were evaluated with the calibration measurements taken on the step-wedge phantom and the CT acquisitions of two phantoms: 1) with iodine tubes at different concentrations and 2) an image quality phantom evaluating noise and ring artifacts. The polynomial approach led to the best spectral accuracy but suffered from high noise. The exhaustive model calibration resulted in divergent behavior and inaccurate spectral quantification. Both reduced parameter models achieved iodine quantification accuracy below 0.5 mg/ml and but only the model with two incident energies per bin presented the least noise and ring artifacts. For this last method the impact of the number of energy bins (2-6) was also evaluated. The configuration with 4, 5 or 6 bins provided superior MD, with accurate iodine quantification and reduced ring artifacts.
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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 | 0.000 |
| 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".