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Source: IEEE Transactions on Biomedical Engineering
Education: Learning
Procedure: MRI Scan
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Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI
Conclusion: The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics. Significance: Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.
Source: IEEE Transactions on Biomedical Engineering - August 21, 2018 Category: Biomedical Engineering Source Type: research
