Progress report on PETSIRD: a standard for PET raw data
Bibliographic record
Abstract
The recent advancement of data science methods in emission tomography (ET) has highlighted the need for standardization in the access, use, and sharing of the unprocessed raw list-mode acquisition data to exploit their high fidelity spatio-temporal information before the production of the respective DICOM images. The Emission Tomography Standardization Initiative (ETSI) was founded in 2022 from an international consortium of academic and industry experts to define open, extendable, and vendor-agnostic ET data formats. ETSI is now focusing on the development of the PETSIRD (“PET Emission Tomography Standardization Initiative’s Raw Data”) prototype: an open standard format for Positron Emission Tomography (PET) list-mode raw data. The components of PETSIRD are (i) its data elements (coincidence events, geometry, correction factors, physiological signal etc.), (ii) a container of the data elements architecture and its access protocols built with the investigational prototype YARDL (Microsoft Research) meta-language, and (iii) a use-cases software toolkit facilitating the basic utility of the standard.In this contribution, we provide a status report of the initiative. Results will be shown of the first ETSI hackathon held in November 2023 as well as progress made on the definition of PETSIRD prototype since then. Up-to-date information can be found on our website https://etsinitiative.org/.
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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 | 0.040 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.019 |
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".