Assessment of intra-aneurysmal flow modification after flow diverter stent placement with four-dimensional flow MRI: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Flow diverter stents (FDS) have been effectively used for the endovascular treatment of sidewall intracranial aneurysms (IAs). Unlike standard endovascular treatments used to exclude directly the aneurysm bulge from the parent vessel, FDS induce reduction in the intra-aneurysmal flow and promote progressive and stable thrombosis therein. The advent of FDS has therefore increased the need for understanding of IA hemodynamics. METHODS: We proposed the use of the most recently evolved four-dimensional (4D) flow MRI technique to evaluate qualitatively and quantitatively post-FDS flow modification in 10 patients. We report intra-aneurysmal velocity measurements and the influence of metal artifacts induced by the stent. RESULTS: An index was defined to quantitatively measure flow changes-namely, the proportional velocity reduction ratio (PVRR)-with ranges from 34.6% to 71.1%. Furthermore, we could compare streamlines characterizing the post-stent flow patterns in five patients in whom the intra-aneurysmal velocity was beyond the visualization threshold of 7.69 cm/s. CONCLUSIONS: Despite metal artifacts and the low velocities involved, 4D flow MRI could be of interest to measure qualitatively and quantitatively flow changes in stented aneurysms. However, further enhancements are required together with further validation work before it can be considered for clinical use.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".