ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): ASL pipeline inventory
Bibliographic record
Abstract
Purpose: To create an inventory of image processing pipelines of arterial spin labeling (ASL) and list their main features, and to evaluate the capability, flexibility, and ease of use of publicly available pipelines to guide novice ASL users in selecting their optimal pipeline. Methods: Developers self-assessed their pipelines using a questionnaire developed by the Task Force 1.1 of the ISMRM Open Science Initiative for Perfusion Imaging. Additionally, each publicly available pipeline was evaluated by two independent testers with basic ASL experience using a scoring system created for this purpose. Results: The developers of 21 pipelines filled the questionnaire. Most pipelines are free for noncommercial use (n = 18) and work with the standard NIfTI (Neuroimaging Informatics Technology Initiative) data format (n = 15). All pipelines can process standard 3D single postlabeling delay pseudo-continuous ASL images and primarily differ in their support of advanced sequences and features. The publicly available pipelines (n = 9) were included in the independent testing, all of them being free for noncommercial use. The pipelines, in general, provided a trade-off between ease of use and flexibility for configuring advanced processing options. Conclusion: Although most ASL pipelines can process the common ASL data types, only some (namely, ASLPrep, ASLtbx, BASIL/Quantiphyse, ExploreASL, and MRICloud) are well-documented, publicly available, support multiple ASL types, have a user-friendly interface, and can provide a useful starting point for ASL processing. The choice of an optimal pipeline should be driven by specific data to be processed and user experience, and can be guided by the information provided in this ASL inventory.
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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 | 0.059 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.022 |
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".