Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Stroke is the second most common cause of death in developed countries. Rapid clinical assessment and intervention have a major impact on preventing infarct growth and consequently on patients' quality of life. Clinical interventions aim to restore perfusion deficits via pharmaceutical or mechanical intervention. Regardless of which reperfusion procedure is used, clinicians need to consider the risks and benefits based on multi-modal neuroimaging studies, such as MRI scans, as well as their own clinical experience. This intricate decision-making process would benefit from an automatic prediction of the final infarct, which would provide a estimation of tissue that will probably infarct. This paper introduces a deep learning method to automatically predict ischemic stroke tissue outcome. The authors propose an end-to-end deep learning architecture that combines information from perfusion dynamic susceptibility MRI, alongside perfusion and diffusion parametric maps. We aim to automatically extract features from the raw perfusion DSC-MRI to further complement the information gleaned from standard parametric maps, and to overcome the loss of information that can occur during perfusion postprocessing. Combining both data types in a single architecture, with dedicated paths, we achieve competitive results when predicting the final stroke infarct core lesion in the publicly available ISLES 2017 dataset.

Automated summary

Stroke is the second most common cause of death in developed countries, and rapid clinical assessment and intervention play a crucial role in improving patients' quality of life. Clinical interventions aim to restore perfusion deficits, and a deep learning method can automatically predict ischemic stroke tissue outcome.