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 (Frontiers in Neurology, 2018)
In developed countries, the second leading cause of death is stroke, whose most common type is the ischemic stroke. 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 forecasting the risks and benefits of the revascularization procedure. However, the decision process is an intricate task due to the variability in lesion sizes, shapes, and locations, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome would provide an important support in the physician’s decision process. 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 sequences with non-imaging clinical information: the thrombolysis in cerebral infarction scale. This scale categorizes the success of revascularization, and consequently the brain blood flow in the presence of a stroke lesion. 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.