How Flow Reduction Influences the Intracranial Aneurysm Occlusion: A Prospective 4D Phase-Contrast MRI Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Flow-diverter stents are widely used for the treatment of wide-neck intracranial aneurysms. Various parameters may influence intracranial aneurysm thrombosis, including the flow reduction induced by flow-diverter stent implantation, which is assumed to play a leading role. However, its actual impact remains unclear due to the lack of detailed intra-aneurysmal flow measurements. This study aimed to clarify this relationship by quantitatively measuring the intra-aneurysmal flow using 4D phase-contrast MR imaging. MATERIALS AND METHODS: We acquired prospective pre- and post-stent implantation 4D phase-contrast MR imaging data of a consecutive series of 23 patients treated with flow-diverter stents. Velocity field data were combined with the intraprocedural 3D angiogram vessel geometries for precise intracranial aneurysm extraction and partial volume correction. Intra-aneurysmal hemodynamic modifications were compared with occlusion outcomes at 6 and 12 months. RESULTS: The averaged velocities at systole were lower after flow-diverter stent implantation for all patients and ranged from 21.7 ± 7.1 cm/s before to 7.2 ± 2.9 cm/s after stent placement. The velocity reduction was more important for the group of patients with aneurysm thrombosis at 6 months (68.8%) and decreased gradually from 66.2% to 55% for 12-month thrombosis and no thrombosis, respectively (P = .08). CONCLUSIONS: We propose an innovative approach to measure intracranial flow changes after flow-diverter stent implantation. We identified a trend between flow reduction and thrombosis outcome that brings a new insight into current understanding of the flow-diversion treatment response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".