Development of novel and low-cost readout electronics for large FOV gamma camera detector
Bibliographic record
Abstract
Abstract Objective: Gamma cameras based on scintillation crystals play a crucial role in nuclear medicine. We designed a readout method for a large field-of-view (FOV) gamma detector, reducing N × M analog signals of photomultiplier tubes (PMTs) to N + M analog sums of each row and column, thus improving complexity and cost considerations while preserving image quality. Methods: In this study, we developed a gamma detector consisting of 48 square PMTs using novel readout electronics, reducing 6 × 8 to 6 + 8 analog signals. All 14 analog signals were converted to digital signals using AD9257 high-speed analog to digital converters (ADC) driven by the SPARTAN-6 family of field-programmable gate arrays (FPGA) in order to calculate the signal integrals. The positioning algorithm was based on the digital correlated signal enhancement (CSE) algorithm, which was implemented in the acquisition software. The performance characteristics of the developed gamma camera were measured using the NEMA NU1 standard. Results: The measured energy resolution in the developed detector was 8.7%, intrinsic spatial resolution was 3.9 mm, uniformity was within 0.6%, and linearity was within 0.1%. Conclusions: The performance evaluation demonstrated that the developed detector has proper specifications for imaging purposes.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".