Multimodal MRI-Based Triage for Acute Stroke Therapy: Challenges and Progress

Frontiers in Neurology

Jeong Pyo Son¹  Oh Young Bang¹  Gyeong-Moon Kim²  Jong-Won Chung²  Woo-Keun Seo²  Yoon-Chul Kim³  Dong-Eog Kim⁴  Wi-Sun Ryu⁴ 

[1] Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea;Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea;Samsung Medical Center, Clinical Research Institute, Seoul, South Korea;Stroke Center and Korean Brain MRI Data Center, Dongguk University Ilsan Hospital, Goyang, South Korea;

关键词: stroke;    MRI;    endovascular treatment;    machine learning;    triage;   

DOI  :  10.3389/fneur.2018.00586

来源: DOAJ

【 摘 要 】

Revascularization therapies have been established as the treatment mainstay for acute ischemic stroke. However, a substantial number of patients are either ineligible for revascularization therapy, or the treatment fails or is futile. At present, non-contrast computed tomography is the first-line neuroimaging modality for patients with acute stroke. The use of magnetic resonance imaging (MRI) to predict the response to early revascularization therapy and to identify patients for delayed treatment is desirable. MRI could provide information on stroke pathophysiologies, including the ischemic core, perfusion, collaterals, clot, and blood–brain barrier status. During the past 20 years, there have been significant advances in neuroimaging as well as in revascularization strategies for treating patients with acute ischemic stroke. In this review, we discuss the role of MRI and post-processing, including machine-learning techniques, and recent advances in MRI-based triage for revascularization therapies in acute ischemic stroke.

【 授权许可】

Unknown