Anti-correlated noise reduction in triple-energy, photon-counting x-ray angiography
Bibliographic record
Abstract
Single-exposure, dual-energy x-ray angiography using photon-counting x-ray detectors is a potential alternative to kV-switching dual-energy angiography and digital subtraction angiography (DSA), but is unable to simultaneously suppress anatomic noise from soft tissue and bone. Triple-energy, photon-counting angiography that counts photons in three energy bins during a single x-ray exposure would overcome this limitation but high quantum noise levels compromise image quality. We extended anti-correlated noise reduction (ACNR) for dual-energy angiography to triple-energy photon-counting angiography and evaluated the resulting improvement in iodine signal-difference-to-noise ratio (SDNR). We implemented triple-energy photon-counting imaging of iodine using a cadmium-telluride, photon-counting x-ray detector with analog charge summing for charge-sharing correction. We imaged a phantom consisting of vessel-like structures with diameters of 3mm, 4mm and 5mm embedded in background clutter. The vessels were filled with an iodine solution containing 100mg/ml of iodine. We acquired images at 60kV, 80kV and 100kV using energy thresholds that theoretically maximized SDNR per root entrance air kerma without ACNR. For each tube voltage, we simulated three energy bins by acquiring two separate exposures using two different sets of energy thresholds. The triple-energy images with ACNR had SDNR that was approximately 7.5 times greater than those without. Further increases in ACNR are expected with optimization of tube voltage and energy thresholds in the presence of ACNR. Future work will focus on optimization and frequency-dependent image-quality assessment.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".