Neurovascular stent artifacts in 3D‐TOF and 3D‐PCMRI: Influence of stent design on flow measurement
Bibliographic record
Abstract
PURPOSE: The morphological and hemodynamic evaluations of neurovascular diseases treated with stents would benefit from noninvasive imaging techniques such as 3D time-of-flight MRI (3D-TOF) and 3D phase contrast MRI (3D-PCMRI). For this purpose, a comprehensive evaluation of the stent artifacts and their impact on the flow measurement is critical. METHODS: The artifacts of a representative sample of neurovascular stents were evaluated in vitro with 3D-TOF and 3D-PCMRI sequences. The dependency of the artifacts with respect to the orientation was analyzed for each stent design as well as the impact on the flow measurement accuracy. Furthermore, the 3D-PCMRI data of four patients carrying intracranial aneurysms treated with flow diverter stents were analyzed as illustrative examples. RESULTS: The stent artifacts were mainly confined to the stent lumen therefore indicating the leading role of shielding effect. The influence of the stent design and its orientation with respect to the transmitting MR coils were highlighted. The artifacts impacted the 3D-PCMRI velocities mainly in the low magnitude domains, which were discarded from the analysis ensuring reliable near-stent velocities. The feasibility of in-stent flow measurements was confirmed in vivo on two patients who showed strong correlation between flow and geometric features. In two other patients, the consistency of out-of-stent velocities was verified qualitatively through intra-aneurysmal streamlines except when susceptibility artifacts occurred. CONCLUSION: The present results motivate the conception of low inductance or nonconductive stent design. Furthermore, the feasibility of near-stent 3D-PCMRI measurements opens the door to clinical applications like the post-treatment follow-up of stenoses or intracranial aneurysms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".