Susceptibility weighted imaging in acute cerebral ischemia: review of emerging technical concepts and clinical applications
Bibliographic record
Abstract
Susceptibility weighted imaging (SWI) is an essential magnetic resonance imaging sequence in the assessment of acute ischemic stroke. In this article, we discuss the physics principals and clinical application of conventional SWI and multi-echo SWI sequences. We review the research evidence and practical approach of SWI in acute ischemic stroke by focusing on the detection and characterization of thromboembolism in the cerebral circulation. In addition, we discuss the role of SWI in the assessment of neuroparenchyma by depiction of asymmetric hypointense cortical veins in the ischemic territory (surrogate tissue perfusion), detection of existing microbleeds before stroke treatment and monitoring for hemorrhagic transformation post-treatment. In conclusion, the SWI sequence complements other parameters in the stroke magnetic resonance imaging protocol and understanding of the research evidence is vital for practising stroke neurologists and neuroradiologists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".