Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information.

Pinto, Adriano; McKinley, Richard; Alves, Victor; Wiest, Roland; Silva, Carlos A; Reyes, Mauricio (2018). Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information. Frontiers in neurology, 9(1060), p. 1060. Frontiers Media S.A. 10.3389/fneur.2018.01060

Preview

Text fneur-09-01060.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview

In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.

Interest & Impact

Downloads

144 since deposited on 04 Feb 2019
16 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item