Evaluation of edge-illumination and propagation-based x-ray phase contrast imaging methods for high resolution imaging application
Bibliographic record
Abstract
The aim of this study is to investigate and reveal the potential of employing a direct conversion amorphous selenium (a-Se) CMOS based high resolution x-ray detector in both propagation-based (PB) and edge illumination (EI) x-ray phase contrast imaging (XPCi) techniques. Both PB-XPCi and EI-XPCi modalities are evaluated through a numerical model and are compared based on their contrast, edge-enhancement, visibility, and dose efficiency characteristics. It is demonstrated how EI-XPCi configuration outperforms the PB-XPCi one considering using the same x-ray source and detector. After highlighting the strength of EI-XPCi system and reviewing today’s XPCi technologies, absorption mask grating fabrication is addressed as the main challenge to upgrade and improve EI-XPCi setups to higher resolution detectors. Mammography is considered as a case study to elucidate the importance of employing a high resolution EI-XPCi technique for microcalcification detection through numerical simulation of a breast phantom.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".