Abstract
Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model of tissue fate that combines regional imaging features available after onset. A key component is the use of cuboids randomly sampled during the learning process. Models trained with time-to-maximum feature (Tmax) computed from perfusion weighted images (PWI) are compared to the ones obtained from the apparent diffusion coefficient (ADC). The prediction task is formalized as a regression problem where the inputs are the local cuboids extracted from Tmax or ADC images at onset, and the output is the segmented FLAIR intensity of the tissue 4 days after intervention. Experiments on 25 acute stroke patients demonstrate the effectiveness of the proposed approach in predicting tissue fate. Results on our dataset show the superiority of the regional model vs. a single-voxel-based approach, indicate that PWI regional models outperform ADC models, and demonstrates that a nonlinear regression model significantly improves the results in comparison to a linear model.
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Notes
The low sagittal resolution (≥7 mm per voxel) of PWI images did not allow us to test the z-size of the cuboid which was set to 1 slice.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (NSFC) to F.S., grant number 31050110122. D.L. was partially supported by NIH/NINDS K23NS054084, P50NS044378, K24NS072272.
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Associate Editor Xiaoxiang Zheng oversaw the review of this article.
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Scalzo, F., Hao, Q., Alger, J.R. et al. Regional Prediction of Tissue Fate in Acute Ischemic Stroke. Ann Biomed Eng 40, 2177–2187 (2012). https://doi.org/10.1007/s10439-012-0591-7
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DOI: https://doi.org/10.1007/s10439-012-0591-7