Low-cost X-ray quality assurance system based on optical sensors and phosphor screen
Bibliographic record
Abstract
Digital radiographic imaging systems are becoming more widely deployed in low-resource settings, potentially reducing the inequitable access to medical imaging that persists today. Even when resources are made available to install digital x-ray equipment, challenges remain with respect to ongoing maintenance and recommended quality assurance programs. Recent studies have indicated that a significant fraction of radiographic installations in Africa are not assessed at recommended intervals due to the lack of high-cost x-ray exposure meters. As a result, errors in x-ray exposure parameters (<i>e.g.</i> current, exposure time, or kilovoltage) can lead to suboptimal image quality, repeated exams, and unnecessary radiation exposure to patients and staff. We have developed a low-cost solution for routine x-ray quality assurance measurements, which takes advantage of commercial-off-the-shelf (COTS) electronic components integrated with a low-power microprocessor controller. The device employs four sensitive phototransistors connected to a multiplexed 16-bit analog-to-digital converter. Light input to the optical sensors is provided by a rare-earth phosphor screen (Lanex regular), which emits green light under exposure to x-rays. Estimation of spectral properties is enabled by the use of aluminum filters on two of the four photosensors. Acquisition is controlled by an open-source microprocessor (Arduino Nano 33 BLE) and the total cost of all components is less than $100 USD. Comparisons against a commercial sensor indicate that the optical-based measurements of x-ray exposure are linear and accurate to within ±3%, over the range from 60 to 140 kVp. This project demonstrates the feasibility of providing accurate, robust solid-state x-ray exposure measurements in low-resource settings.
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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".