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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

C2MA-Net: Cross-Modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation Based on CT Perfusion Scans
Conclusion: This study demonstrates advantages of applying C2MA-network to segment AIS lesions, which yields promising segmentation accuracy, and achieves semantic decoupling by processing different parameter modalities separately. Significance: Proving the potential of cross-modal interactions in attention to assist identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies.
Source: IEEE Transactions on Biomedical Engineering - December 24, 2021 Category: Biomedical Engineering Source Type: research