Recent stroke trials raised a demand for triage decision intelligence of ischemic lesion progression. This study aimed to develop a multiparametric deep neural network to segment regions that predicted final infarct formation. The PWI-derived CBF, CBV, MTT and Tmax maps served as multi-channel inputs to algorithm training. We used a 2.5D U-Net to generate lesion segmentation. Our approach showed a good sensitivity and specificity with AUC of 0.868 in predicting the final lesions, and a comparable performance of DICE and IOU. In conclusion, we demonstrated feasibility for predicting tissue outcome in acute ischemic stroke with multiparametric deep learning algorithm
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords