Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
This work addresses stroke lesion outcome prediction for physicians, offering an incremental improvement by enhancing existing clinical methods with data-driven modeling.
The authors tackled the problem of predicting ischemic stroke lesion outcomes by fusing standard clinical perfusion maps with data-driven maps from raw 4D PWI images using deep learning, resulting in improved prediction of tissue at risk compared to using only standard maps.
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life. To perform the revascularization procedure, the decision making of physicians considers its risks and benefits based on multi-modal MRI and clinical experience. Therefore, automatic prediction of the ischemic stroke lesion outcome has the potential to assist the physician towards a better stroke assessment and information about tissue outcome. Typically, automatic methods consider the information of the standard kinetic models of diffusion and perfusion MRI (e.g. Tmax, TTP, MTT, rCBF, rCBV) to perform lesion outcome prediction. In this work, we propose a deep learning method to fuse this information with an automated data selection of the raw 4D PWI image information, followed by a data-driven deep-learning modeling of the underlying blood flow hemodynamics. We demonstrate the ability of the proposed approach to improve prediction of tissue at risk before therapy, as compared to only using the standard clinical perfusion maps, hence suggesting on the potential benefits of the proposed data-driven raw perfusion data modelling approach.