Time discrimination techniques using artificial neural networks for positron emission tomography
Bibliographic record
Abstract
Relevant information in positron emission tomography (PET) is currently being obtained mostly by analog signal processing methods. New digital PET scanner architectures are now becoming available, which offer greater flexibility and easier reconfiguration capability as compared to previous PET designs. Moreover, new strategies can be devised to extract more information with better accuracy from the digitized detector signals. Trained artificial neural networks (ANN) have been investigated to improve coincidence timing resolution with different types of APD-based detectors. The signal at the output of a charge sensitive preamplifier was digitized with an off-the-shelf, free-running 100-MHz, 8-bit ADC and time discrimination was performed with ANNs implemented in field programmable gate arrays (FPGA). Results show that ANNs can be particularly efficient with slow and low light output scintillators like BGO (/spl tau/=300 ns), but less so with faster luminous crystals such as LSO (/spl tau/=40 ns). In reference to a fast PMT-plastic detector, a time resolution of 6.5 ns was achieved with a BGO-APD detector, as compare to 12.7 ns with conventional analog methods using a constant fraction discriminator. With LSO, the ANN was found to be competitive with other digital techniques developed in previous works. In conclusion, ANNs implemented in FPGAs provide a fast and flexible circuit that can be easily reconfigured to accommodate various detectors under different signal/noise conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".