Survey of the American Society of Neuroradiology Membership on the Use and Value of Intracranial Vessel Wall MRI
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Intracranial vessel wall MR imaging is an emerging technique for intracranial vasculopathy assessment. Our aim was to investigate intracranial vessel wall MR imaging use by the American Society of Neuroradiology (ASNR) members at their home institutions, including indications and barriers to implementation. MATERIALS AND METHODS: The ASNR Vessel Wall Imaging Study Group survey on vessel wall MR imaging use, frequency, applications, MR imaging systems and field strength used, protocol development approaches, vendor engagement, reasons for not using vessel wall MR imaging, ordering-provider interest, and impact on clinical care, was distributed to the ASNR membership between April 2 and August 30, 2019. RESULTS: There were 532 responses; 79 were excluded due to nonresponse and 42 due to redundant institutional responses, leaving 411 responses. Fifty-two percent indicated that their institution performs vessel wall MR imaging, with 71.5% performed at least 1-2 times/month, most frequently on 3T MR imaging, and 87.7% using 3D sequences. Protocols most commonly included were T1-weighted pre- and postcontrast and TOF-MRA; 60.6% had limited contributions from vendors or were still in protocol development. Vasculopathy differentiation (94.4%), cryptogenic stroke (41.3%), aneurysm (38.0%), and atherosclerosis (37.6%) evaluation were the most common indications. For those not performing vessel wall MR imaging, interpretation (53.1%) or technical (46.4%) expertise, knowledge of applications (50.5%), or limitations of clinician (56.7%) or radiologist (49.0%) interest were the most common reasons. If technical/expertise obstacles were overcome, 56.4% of those not performing vessel wall MR imaging indicated that they would perform it. Ordering providers most frequently inquiring about vessel wall MR imaging were from stroke neurology (56.5%) and neurosurgery (25.1%), while 34.3% indicated that no providers had inquired. CONCLUSIONS: More than 50% of neuroradiology groups use vessel wall MR imaging for intracranial vasculopathy characterization and differentiation, emphasizing the need for additional technical and educational support, especially as clinical vessel wall MR imaging implementation continues to grow.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".