Cerebrovascular MRI: a review of state‐of‐the‐art approaches, methods and techniques
Bibliographic record
Abstract
Cerebrovascular imaging is of great interest in the understanding of neurological disease. MRI is a non-invasive technology that can visualize and provide information on: (i) the structure of major blood vessels; (ii) the blood flow velocity in these vessels; and (iii) the microcirculation, including the assessment of brain perfusion. Although other medical imaging modalities can also interrogate the cerebrovascular system, MR provides a comprehensive assessment, as it can acquire many different structural and functional image contrasts whilst maintaining a high level of patient comfort and acceptance. The extent of examination is limited only by the practicalities of patient tolerance or clinical scheduling limitations. Currently, MRI methods can provide a range of metrics related to the cerebral vasculature, including: (i) major vessel anatomy via time-of-flight and contrast-enhanced imaging; (ii) blood flow velocity via phase contrast imaging; (iii) major vessel anatomy and tissue perfusion via arterial spin labeling and dynamic bolus passage approaches; and (iv) venography via susceptibility-based imaging. When designing an MRI protocol for patients with suspected cerebral vascular abnormalities, it is appropriate to have a complete understanding of when to use each of the available techniques in the 'MR angiography toolkit'. In this review article, we: (i) overview the relevant anatomy, common pathologies and alternative imaging modalities; (ii) describe the physical principles and implementations of the above listed methods; (iii) provide guidance on the selection of acquisition parameters; and (iv) describe the existing and potential applications of MRI to the cerebral vasculature and diseases. The focus of this review is on obtaining an understanding through the application of advanced MRI methodology of both normal and abnormal blood flow in the cerebrovascular arteries, capillaries and veins.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".