Preliminary results of an automatic channel fault detection system on a small animal APD-based digital PET scanner
Bibliographic record
Abstract
APD-based positron emission tomography scanners show enhanced imaging capabilities in terms of spatial resolution and contrast due to the one to one coupling and size of individual crystal-APD detectors. However, to ensure maximum performance, these PET scanners require proper calibration by qualified professionals, which can become a cumbersome task because of the large number of channels present. An intelligent system (IS) intends to alleviate this workload by enabling a diagnosis of the observational errors of the scanner. The IS can be broken down into four hierarchical blocks: parameter extraction, channel fault detection, prioritization and diagnosis. One of the main function of the IS consists of analyzing available channel data such as: normalization coincidence counts and single count rates, crystal identification classification data, energy histograms, APD bias and noise thresholds to establish the channel's health status that will be used to detect channel faults. This paper focuses on the first two blocks of the IS: parameter extraction and channel fault detection. The purpose of the parameter extraction block is to process available channel data into parameters that are subsequently used by the fault detection block to generate the channel's health status. To ensure extensibility, the channel fault detection block is divided into indicators representing different aspects of the PET scanner performance: sensitivity, timing, identification and energy. Some experiments on a LabPET scanner located at the Sherbrooke Molecular Imaging Center demonstrated an erroneous channel fault detection rate of 6% (with a 95% confidence interval (CI) of: [5, 6]) which is considered tolerable. Globally, the IS achieves a channel fault detection efficiency of 95% (CI: [92, 97]), which proves many faults can be detected automatically. Increased fault detection efficiency would be advantageous but, the achieved results would already benefit professionals in their maintenance task.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".