Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction

TL;DR

This paper introduces a deep learning approach that combines standard MRI perfusion maps with raw 4D PWI data to improve prediction of ischemic stroke lesion outcomes, potentially aiding clinical decision-making.

Contribution

It presents a novel data-driven deep learning method that fuses standard perfusion maps with raw PWI data for better lesion outcome prediction.

Findings

Improved prediction accuracy over standard perfusion maps.

Demonstrated potential for better stroke outcome assessment.

Enhanced modeling of blood flow hemodynamics.

Abstract

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.