Towards Reproducible and Resource‐Efficient Perfusion Imaging Analysis for African Dementia Imaging Research
Bibliographic record
Abstract
BACKGROUND: Arterial spin labelling (ASL) is an established magnetic resonance imaging (MRI) technique for non-invasive assessment of cerebral blood flow (CBF). However, the lack of expertise and the costly computational resources required to analyze ASL data are major barriers to its use in resource-constrained settings (RCS), particularly in Africa. ASL-MRICloud was recently introduced as the only cloud-based open-source option for ASL processing that requires no installation on local computers, making it suited for ASL analysis in RCS. METHODS: In this work, we implemented ASL-MRICloud in Google Colab to perform data analysis at scale and minimal cost, with the aim of enhancing population studies reproducibility in RCS. This work was performed as a training exercise of the CONNExIN (COmprehensive Neuroimaging aNalysis Experience In resource-constraiNed Settings) Program, a neuroimage analysis training program for African researchers. A team of CONNExIN participants leveraged the Open Science Initiative for Perfusion Imaging ASL (OSIPI-ASL) MRI Challenge dataset (n = 10) to test the implemented ASL-MRICloud Google Colab. The pipeline included a data preparation step for data conversion and generation of parameter files to be used for data processing. ASL processing and CBF quantification were then performed using automated steps in ASL-MRICloud to generate whole-brain CBF maps and extract preset regional values. The pipeline was validated by comparing the CBF maps and regional values to their ground-truth, ASL-MRICloud developer analysis, and results from other established ASL processing tools (Oxford ASL and Quantiphyse). The Google Colab implementation was provided to four CONNExIN teams to replicate the challenge data processing and analyze the dataset of 75 subjects from the PREVENT-AD (PResymptomatic EValuation of Experimental or Novel Treatments for AD) study. RESULTS: Preliminary results from the simulated OSIPI-ASL dataset generated from our pipeline are shown in Figure 1. The pipeline, documentation, and results of the analysis from the four teams will be made publicly available on Protocol.io. CONCLUSION: Using simulated (OSIPI-ASL) and real-world (PREVENT-AD) data, we aim to assess the feasibility of a resource-efficient image processing tool for reproducible ASL data analysis. Once implemented, we will share our approach for wider use in RCS to enable inclusive and reproducible imaging research.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".