On the detectability and accuracy of computational measurements of enlarged perivascular spaces from magnetic resonance images
Bibliographic record
Abstract
Abstract Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. Therefore, several computational methods have been developed for their assessment from brain MRI. But the limits of validity of these methods under various spatial resolutions, and the accuracy in detecting and measuring the dimensions of these structures have not been established. We use a digital reference object (DRO) previously developed for this purpose, to construct an in-silico phantom for answering these questions; and validate it using a physical phantom. Our in-silico and physical phantoms use cylinders of different sizes as models for PVS. Using both phantoms, we also evaluate the influence of the “PVS” orientation on the accuracy of the diameter measured, different sets of parameters for two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, and the influence of the vesselness filter per-se in the accuracy of the measurements. Our experiments indicate that PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter for this task relies on a careful tuning of several parameters. The combination of parameters α=0.5, β=0.5 and c=500 proved to yield the best results. RORPO, on the contrary, does not have these requirements, and allows detecting smaller cylinders in their entirety more consistently in the ideal scenarios tested. The segmentation of the cylinders using the Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. “PVS” orientation did not influence their measures for image data with isotropic voxel size. Further evaluation of the emerging deep-learning methods is still required, and these results should be tested in “real” world data across several diseases.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".