Frequency-dependent MTF and DQE of photon-counting x-ray imaging detectors (Conference Presentation)
Bibliographic record
Abstract
Theoretical modeling of the performance of x-ray imaging detectors enables understanding relationships between the physics of x-ray detection and x-ray image quality, and enables theoretical optimization of novel x-ray imaging techniques and technologies. We present an overview of a framework for theoretical modeling of the frequency-dependent signal and noise properties of single-photon-counting (SPC) and energy-resolving x-ray imaging detectors. We show that the energy-response function, large-area gain, modulation transfer function (MTF), noise power spectrum (NPS) (including spatio-energetic noise correlations) and detective quantum efficiency (DQE) of SPC and energy-resolving x-ray imaging detectors are related through the probability density function (PDF) describing the number electron-hole (e-h) pairs collected in detector elements following individual x-ray interactions. We demonstrate how a PDF-transfer approach can be used to model analytically the MTF and NPS, including spatio-energetic noise correlations, of SPC and energy-resolving x-ray imaging detectors. Our approach enables modeling the combined effects of stochastic conversion gain, electronic noise, characteristic emission, characteristic reabsorption, coulomb repulsion and diffusion of e-h pairs and energy thresholding on the MTF and NPS. We present applications of this framework to (1) analysis of the frequency-dependent DQE of SPC systems that use cadmium telluride (CdTe) x-ray converters, and (2) analysis of spatio-energetic noise correlations in CdTe energy-resolving x-ray detectors. The developed framework provides a platform for theoretical optimization of next-generation SPC and energy-resolving x-ray imaging detectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".