MO‐D‐201B‐04: Emerging X‐Ray Detector Technologies
Bibliographic record
Abstract
The main topic of this Symposium talk is new development of selenium based x‐ray imaging detectors. Some of these new developments include: 1. Very high‐resolution direct‐conversion detectors for breast imaging; 2. Large area detector with low‐noise CMOS readout; 3. Large‐area indirect conversion flat‐panel imager with avalanche gain obtained with an optically sensitive selenium layer; 4. Possible approaches for 2D photon‐counting detector with selenium. The physics of direct and indirect conversion x‐ray imaging detectors will be described, and the limitations of existing detector technologies outlined. The basic principle of operation of the emerging detector technologies will be explained, and the rationales for their improved performance and potential clinical applications will be summarized. Learning Objectives: 1. Understand the basic physics of x‐ray imaging detectors using either direct or indirect conversion 2. Understand the factors affecting imaging performance, and the limitations of different detector approaches 3. Learn the advantages of some emerging x‐ray imaging detector technologies that can address existing problems
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".