Portable Single-Exposure Dual-Energy X-ray Detector for Improved Point-of-Care Diagnostic Imaging
Bibliographic record
Abstract
INTRODUCTION: Dual-energy subtraction (DES) imaging is well known to reduce anatomical noise and enable material classification. The current approaches to DES imaging have trade-offs, such as motion artifacts, low sensitivity because of losses in a mid-filter, and lack of portability. Recently, a portable triple-layer flat-panel detector (FPD) was proposed for use in single-shot DES imaging that can provide improved sensitivity and removal of motion artifacts in a point-of-care setting. The purpose of this study is to evaluate the feasibility of such a detector. Various image quality metrics and clinical images are provided. MATERIALS AND METHODS: An FDA-cleared single-exposure DES FPD consisting of three stacked sensors was used for all measurements. This detector generates three images per exposure: A digital radiography (DR) image, i.e., as would be produced with a conventional detector, and two DES images, bone and soft tissue. To evaluate DR image quality, detective quantum efficiency (DQE) and modulation transfer function were measured for multiple radiation quality beams. Digital radiography and DES images obtained from this FPD were evaluated in previously reported fixed and portable clinical studies. Digital radiography and DES images from case studies are presented for qualitative assessment. RESULTS: Modulation transfer function and DQE were measured across multiple radiation quality beams for the DR image. The DES images showed good tissue separation and uniformity with no visible motion or alignment artifacts. The DES images, when read in conjunction with the DR image, resulted in increased reader confidence and revealed abnormalities or details that were sometimes overlooked in the DR image. CONCLUSIONS: The proposed panel produces high-quality DR images as indicated by the DQE and modulation transfer function. The DES images have been shown to improve sensitivity in clinical applications and increase reader confidence. This detector can enable DES in portable or otherwise difficult applications, opening new doors for improved patient care.
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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 | 0.001 |
| 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".