Resolution enhancement of computed radiography images using two orthogonal tilts
Bibliographic record
Abstract
Limitations to the spatial resolution of current digital x-ray systems are bounded by the physical characteristics of the xray detector. However, the need to image smaller structures provides motivation to develop high-resolution x-ray detector systems for use with computed radiographic, and tomographic x-ray systems. We report the implementation of a tilted detector technique (TDT) to attain near isotropic resolution enhancement by combining two orthogonal image views, acquired with existing detector hardware tilted at a fixed angle. Images were acquired using a ceiling-mounted x-ray unit (Proteus XR/a, GE Medical Systems, 50kVp, 250mAs). Images were digitized using a Fujifilm Capsula X CR system, from a 35×43cm detector cassette placed on an angulated stand, featuring a 3520×4280 image matrix with an in-plane pixel spacing of 0.1mm. Three images were acquired: two for use with our TDT; and one for comparison, with no detector tilt. Performance was determined by using two line-pair phantoms (Models 07-521 and 07-533, Nuclear Associates) placed orthogonally to each other in the field of view. Custom software corrected for perspective distortion, co-registered and combined the tilted-detector images into a single higher-resolution image. Following unwarping and co-registration, the limiting spatial resolution of an image obtained via the weighted combination of the two orthogonal views (8 lp/mm) is found to be superior to that of a single view acquired with no detector tilt (5 lp/mm). This novel technique shows significant improvement in the spatial resolution of x-ray image acquisitions, using existing x-ray components and detector hardware.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".