Experimental optimization of photon-counting dual-energy thoracic imaging
Bibliographic record
Abstract
Dual-energy (DE) thoracic imaging with photon counting detectors (PCDs) enables suppressing anatomic noise due to bone structures, potentially improving the detection of lung nodules. Relative to DE approaches based on the kV-switching method, a PCD-based, single-exposure approach may reduce the potential for motion artifacts and suppress electronic noise. In this work, we experimentally optimize the energy thresholds and tube voltages for single-exposure DE thoracic imaging implemented with PCDs. We used a phantom consisting of uniform acrylic slabs totaling 8.6 cm in thickness with a 16 cm air gap between slabs. A lung nodule was simulated by adding 2 mm of acrylic in a small region of interest in the center of the phantom. We imaged the phantom using a PCD with a 750-μm-thick cadmium telluride x-ray convertor, 100×100 μm2 detector elements, two user-defined energy thresholds, and analog charge summing for charge sharing correction. We acquire images for tube voltages ranging from 90 kV to 130 kV in 10-kV increments. For each tube voltage, we varied the energy threshold from 32 keV to 90% of the maximum beam energy. The contrast-to-noise ratio (CNR) of the simulated nodule was measured for each combination of tube voltage and energy threshold. Optimal energy thresholds range from 45 keV to 55 keV for tube voltages ranging from 110 kV to 120 kV. When optimized for the energy thresholds, there was relatively little variability in the CNR across tube voltages beyond that due to measurement error. Future work will compare the CNR of PCD-based DE radiography with the conventional kV-switching approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".