ARMAX model and recursive least-squares identification for DOI measurement in PET
Bibliographic record
Abstract
Several attempts have been made at measuring DOI in PET scanners. Most solutions offer poor performance in noise. Others are overly complex in view of the information recovered. Most implementations are also impractical, if feasible at all, in APD-based, digital, small animal scanners using multilayer scintillation detectors. The computing power and low cost of modern digital electronics allows the use of more advanced techniques. This paper proposes a novel method derived from control theory that abstracts the scanner acquisition front-end measurement system into a single model. It fits an AutoRegressive Moving-Average with noise (ARMAX) model to the measured data using a Recursive Least-Squares (RLS) identification algorithm, with excellent performance in heavy noise. DOI is subsequently discriminated by the locus of identified poles and zeros onto a complex digital frequency map. More advanced decision heuristics can be used when the detector scintillation layers have too similar light output dynamics. The implementation of this algorithm in PET benefits from extensive a priori knowledge of the system, resulting in significant simplifications. The identification engine is realized on programmable logic chips (FPGA), is pipelined, is running at 100 MHz and is time-shared between several detectors. Preliminary simulations show near perfect discrimination of the scintillation layer. This paper discusses the theory, the implementation and the pros and cons of that method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".