Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images

King Chung Ho; Fabien Scalzo; Karthik V. Sarma; William Speier; Suzie El-Saden; Corey Arnold

doi:10.1117/1.JMI.6.2.026001

22 May 2019 Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images

King Chung Ho, Fabien Scalzo, Karthik V. Sarma, William Speier, Suzie El-Saden, Corey Arnold

Author Affiliations +

Journal of Medical Imaging, Vol. 6, Issue 2, 026001 (May 2019). https://doi.org/10.1117/1.JMI.6.2.026001

Abstract

Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871 ± 0.024, outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.

Citation Download Citation

King Chung Ho, Fabien Scalzo, Karthik V. Sarma, William Speier, Suzie El-Saden, and Corey Arnold "Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images," Journal of Medical Imaging 6(2), 026001 (22 May 2019). https://doi.org/10.1117/1.JMI.6.2.026001

Received: 10 January 2019; Accepted: 18 April 2019; Published: 22 May 2019

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