An Amorphous Selenium Based Positron Emission Mammography Camera with Avalanche Gain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early diagnosis of breast cancer is crucial for effective treatment, and the need exists for greater detection ability and specificity than possible by screening x-ray mammography (currently the primary imaging technique for the detection of breast lesions). Positron Emission Tomography (PET) using the radiotracer 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (FDG) offers a noninvasive, highly sensitive method for the diagnosis of breast cancer. Images from PET contain unique metabolic information that is not available from anatomical imaging techniques. We propose a Positron Emission Mammography (PEM) imaging system that maintains the established high specificity of FDG PET while providing improved collection efficiency for the radiotracer signal and the potential for images with better spatial resolution. This PEM system will enable detection of lesions that are considerably smaller than those that can be visualized using whole body PET imaging. The compact dual-head PEM camera will be based on an amorphous selenium (a-Se) avalanche photodetector and the scintillator lutetium oxyorthosilicate (LSO). The camera promises high collection efficiency by combining the fast scintillation light decay and high light yield of LSO with the excellent quantum efficiency, large avalanche gain, and rapid response time of a-Se. We have measured the gain and readout time of an 8 microm a-Se layer and demonstrated the feasibility of the proposed PEM camera.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it