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 (e.g. 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".