An experimental framework for assessing the detective quantum efficiency of spectroscopic x‐ray detectors
Bibliographic record
Abstract
Abstract Background Assessing the performance of spectroscopic x‐ray detectors (SXDs) requires measurement of the frequency‐dependent detective quantum efficiency (DQE). Analytical expressions of the task‐based DQE and task‐independent DQE of SXDs have been presented in the literature, but standardizable experimental methods for measuring them have not. The task‐based DQE quantifies the efficiency with which an SXD uses the x‐ray quanta incident upon it to either quantify or detect a basis material (e.g., soft tissue or bone) of interest. The task‐independent DQE is akin to the conventional DQE in that it is independent of the basis material to be detected or quantified. Purpose The purpose of this paper is to develop an experimental framework to present a method for experimental analysis of the DQE of SXDs, including the task‐based DQE and task‐independent DQE. Methods We develop methods to measure the frequency‐dependent DQE for task of quantifying or detecting a perturbation in a known basis material. We also develop methods for measuring a task‐independent DQE. We show that the task‐based DQEs and the task‐independent DQE can be measured using a modest extension of the methods prescribed by International Electrotechnical Commission (IEC). Specifically, measuring the task‐independent DQE requires measuring the modulation transfer function (MTF) and noise power spectrum (NPS) of each energy‐bin image, in addition to the cross NPS between energy‐bin images. Measuring the task‐based DQEs requires an additional measurement of the transmission fraction through a thin basis‐material absorber. We implemented the developed methods using standardized IEC x‐ray spectra, aluminum (Al) and polymethyl methacrylyte (PMMA) basis materials, and a cadmium telluride (CdTe) SXD equipped with two energy bins and analog charge summing (ACS) for charge‐sharing suppression. We also performed a regression analysis to determine whether or not the task‐independent DQE is predictive of the task‐based DQEs. Results Experimental results of the task‐based DQEs were consistent with simulation results presented in the literature. In general, and as expected, ACS increased the task‐based DQEs and task‐independent DQE. This effect was most pronounced for quantification tasks, in some instances yielding a five‐fold increase in the DQE. For both spectra, with and without ACS for charge sharing correction, the task‐based DQEs were linearly related to the task‐independent DQE, as demonstrated by R 2 ‐values ranging from 0.89 to 1.00. Conclusions We have extended experimental DQE analysis to SXDs that count photons in multiple energy bins in a single x‐ray exposure. The developed framework is an extension of existing IEC methods, and provides a standardized approach to assessing the performance of SXDs.
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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".