Development of a portable instrument for automated measurements of the detective quantum efficiency of x-ray detectors
Bibliographic record
Abstract
The scientific community has generally adopted use of the modulation transfer function (MTF) and detective quantum efficiency (DQE) as primary measures of performance of radiographic detectors. However, measurement of these parameters is generally restricted to experts in laboratory environments due to the required x-ray physics knowledge, specialized instrumentation and computational analyses. We have developed a prototype instrument that automates both the physical measurement and subsequent image analysis to determine the MTF, noise power spectrum (NPS) and DQE of radiographic and mammographic systems. The instrument is placed in the x-ray path directly in front of the detector. A series of images are acquired, saved in "raw" DICOM format and then used to determine the MTF (using the slanted-edge method) and NPS. The number of incident quanta is calculated from measurements of the incident exposure including corrections for air temperature and pressure and ionization chamber spectral response. The primary sources of error are backscatter from the detector and scatter generated within the instrument. These have been minimized to achieve an incident exposure measurement within 2% of a calibrated electrometer and chamber in free space. The MTF and DQE of a commercial CsI-based flat-panel detector were measured over a range of incident exposures from 20 uR to 20 mR per image. Results agreed with both our own laboratory measurements and previously published measurements performed elsewhere with a similar detector within 2% for the MTF and 5% for the DQE. A complete DQE analysis of a clinical digital flat-panel detector is completed in 30 minutes and requires no system modifications.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".