According to the latest global health statistics from World Health Organization, stroke kills nearly 5.7 million people globally, making it one of the leading causes of death and disability worldwide. A significant majority of stroke cases are ?Embolic? in nature ? caused due to the occlusion of a cerebral artery by a fragmented clot or other debris, referred to as an Embolus. Despite its severity, confirmed diagnosis of embolic stroke etiology (source of embolism) remains a key challenge for standard imaging and diagnostic protocols. This is often due to multiple co-existing potential embolism sources in a patient, and an incomplete understanding of embolisms from several cardiac and arterial sources. Lack of a confirmed etiology diagnosis further complicates treatment strategy for recurring strokes ? a common feature in embolic strokes ? thereby reducing treatment efficacy and affecting patient health. These factors indicate a clear need to devise techniques to help discern etiology diagnosis for embolic strokes beyond current standard-of-care imaging and diagnostics. Here we propose to address this need based on a central idea that a comprehensive understanding of embolus transport from various sources to the brain across the heart-brain arterial network is the key to discern etiology in stroke diagnosis. While imaging provides information on location and extent of stroke, they cannot sufficiently elucidate how emboli from a specific source reach the disease site ? which constitutes a missing link for diagnosis. Our past work has led to the development of an innovative computational tool that enables patient- specific modeling of embolus transport from heart and large arteries to the brain. This tool has provided rich quantitative information on embolus transport in arteries, and the three-way synergy between anatomy, hemodynamics, and embolus source/properties which determine embolic stroke risk. Based on these findings, this NIBIB Trailblazer project aims to integrate standard-of-care imaging with computational embolus transport models, to develop an in silico mapping of the heart-brain arterial pathway for embolus transport in stroke. Our development efforts have been organized into three specific aims.
Aim 1 involves developing a comprehensive image-processing framework that extracts quantitative high-dimensional feature data from standard patient images.
Aim 2 involves developing techniques to systematically integrate image-derived quantitative data into the computational embolus transport model.
Aim 3 involves generating a clinically relevant, and validated, in silico mapping of heart-brain embolus transport pathway using our data-integrated computational model. If successful, this mapping will provide clinically relevant information that supplements current diagnostic protocols and enable disambiguating embolism source and strengthening etiology diagnosis. These advances will further enable a new paradigm in stroke treatment via comprehensive integration of standard imaging and clinical data with quantitative in silico tools, thereby improving patient care in stroke.
Embolic stroke affects millions of people worldwide, contributing to one of the leading causes of death and disability globally. However, current imaging and diagnostic protocols often cannot confirm diagnosis of the origin of embolism (stroke etiology), thereby reducing treatment efficacy and affecting patient health. This project will develop a novel computational (in silico) tool that provides detailed quantitative information on embolic stroke risks from various sources in a patient, supplementing current protocols with clinically relevant information that will help disambiguate embolism source, strengthen embolic stroke diagnosis, and thereby significantly advance patient-care in stroke.
