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- W2914904844 abstract "Introduction: CT Perfusion (CTP) imaging allows estimations of the ischemic core and penumbra. We hypothesize that a deep learning approach improves upon classic deconvolution analysis. Method: We trained a deep neural network to predict the final infarct volume based on the native CTP images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN study). Given a CTP scan, prediction of the final infarct with simulated treatment parameters (instant reperfusion or no reperfusion at all), allows estimation of core and perfusion lesion. The model was validated in a five-fold cross-validation on the derivation cohort and, without retraining, on an external dataset (CRISP study). We calculated the mean absolute difference (MAD) between the volumes of the predicted core/perfusion lesion and true final infarct in reperfusers and non-reperfusers (based on 24h MRA and mTICI at the end of the procedure: 0 vs 2B-3) and compared these to the volume estimations of CTP processing by IschemaView RAPID. We assumed the measured final infarct to correspond to the core at baseline in reperfused patients and to the perfusion lesion in non-reperfused patients. Results: In the derivation cohort the deep learning prediction improved core prediction in reperfusers (MAD of 34 ml vs 66 ml, p<0.001) and provided equivalent results in non-reperfusers for perfusion lesion prediction (both MAD of 35ml, p=0.5). Validation on the CRISP dataset showed improved core prediction (MAD of 32 ml vs 37 ml, p<0.001) and was inconclusive for non-reperfusers (n=3). Conclusion: We developed and validated a deep learning based method which improved volume estimations compared to classic CTP processing." @default.
- W2914904844 created "2019-02-21" @default.
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- W2914904844 date "2019-02-01" @default.
- W2914904844 modified "2023-10-18" @default.
- W2914904844 title "Abstract TP80: Deep Learning Based Prediction of Tissue Status From Native CT Perfusion Images" @default.
- W2914904844 doi "https://doi.org/10.1161/str.50.suppl_1.tp80" @default.
- W2914904844 hasPublicationYear "2019" @default.
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