Improving Calibration of Photon Counting Detector CT Using Redundant Sampling
Bibliographic record
Abstract
Ring artifacts are a major obstacle that prevent the easy adoption of photon counting detectors (PCDs) into next-generation CT scanners. PCDs use a direct conversion semiconductor rather than an indirect conversion scintillator, but the stability of common direct conversion semiconductors (CdTe, CZT) remains limited. Small changes in sensitivity between neighboring pixels are amplified in CT reconstruction and result in ring artifacts in PCD CT images. To reduce these artifacts, we propose that pixels be dynamically autocalibrated to each other during the scan. This is done by measuring line integrals with two neighboring detector pixels and building a model of the differences between these two pixels. This can be done practically using flying focal spot, which minimizes the temporal offset between the two datasets. In this work, we demonstrate the proof of principle with a detector shift. The two resulting sinograms are used in an optimization problem that estimates gain factors on a per-pixel level. Preliminary results in a chicken thigh specimen and resolution phantom show strong reductions in ring artifact by about 75%.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".