WE‐G‐204‐05: Relative Object Detectability Evaluation of a New High Resolution A‐Se Direct Detection System Compared to Indirect Micro‐Angiographic Fluoroscopic (MAF) Detectors
Bibliographic record
Abstract
Purpose: To evaluate the task specific imaging performance of a new 25µm pixel pitch, 1000µm thick amorphous selenium direct detection system with CMOS readout for typical angiographic exposure parameters using the relative object detectability (ROD) metric. Methods: The ROD metric uses a simulated object function weighted at each spatial frequency by the detectors’ detective quantum efficiency (DQE), which is an intrinsic performance metric. For this study, the simulated objects were aluminum spheres of varying diameter (0.05–0.6mm). The weighted object function is then integrated over the full range of detectable frequencies inherent to each detector, and a ratio is taken of the resulting value for two detectors. The DQE for the 25µm detector was obtained from a simulation of a proposed a‐Se detector using an exposure of 200µR for a 50keV x‐ray beam. This a‐Se detector was compared to two microangiographic fluoroscope (MAF) detectors [the MAF‐CCD with pixel size of 35µm and Nyquist frequency of 14.2 cycles/mm and the MAF‐CMOS with pixel size of 75µm and Nyquist frequency of 6.6 cycles/mm] and a standard flat‐panel detector (FPD with pixel size of 194µm and Nyquist frequency of 2.5cycles/mm). Results: ROD calculations indicated vastly superior performance by the a‐Se detector in imaging small aluminum spheres. For the 50µm diameter sphere, the ROD values for the a‐Se detector compared to the MAF‐CCD, the MAF‐CMOS, and the FPD were 7.3, 9.3 and 58, respectively. Detector performance in the low frequency regime was dictated by each detector's DQE(0) value. Conclusion: The a‐Se with CMOS readout is unique and appears to have distinctive advantages of incomparable high resolution, low noise, no readout lag, and expandable design. The a‐Se direct detection system will be a powerful imaging tool in angiography, with potential break‐through applications in diagnosis and treatment of neuro‐vascular disease. Supported by NIH Grant: 2R01EB002873 and an equipment grant from Toshiba Medical Systems Corporation
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".