Towards a standard for PET raw data
Bibliographic record
Abstract
As the capabilities of computer hardware and software progresses, the importance of standardized data is becoming more relevant. While DICOM is widely used as a standard for PET images, no agreed format exists for PET raw data. In addition, all current vendor formats differ, to accommodate different architectures and processing strategies.An international working group comprised of PET imaging experts from academia and industry was formed in 2022 to propose a new standard format. Efforts include establishing the key informational elements to include in the standard, data container formats, and integration initiatives. An important feature of the proposal is the use of a meta-language (called Yardl) for defining data structure and protocols for accessing, transferring, and storing data, allowing logical separation of data elements and container formats. All portions of the format, tools for accessing the data in the standardized format, and example data will be made open source and publicly available.In this work, we will provide a status report of the progress of the working group. The effort is ongoing, and we welcome interest, feedback, and support from the greater community. We hope that this standard will facilitate a new paradigm for PET innovation, including new opportunities for inter-scanner and inter-vendor harmonization and AI applications, and aide in the advancement of novel PET applications and analysis tools.
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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 | 0.105 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".