Redefining Hemodynamic Imaging in Stroke: Perfusion Parameter Map Generation from TOF-MRA using Artificial Intelligence

Author:

Kossen Tabea,Madai Vince I,Lohrke Felix,Aydin Orhun Utku,Behland Jonas,Hilbert Adam,Mutke Matthias A,Bendszus Martin,Sobesky Jan,Frey Dietmar

Abstract

AbstractBackgroundPerfusion assessment in cerebrovascular disease is essential for evaluating cerebral hemodynamics and guides many current treatment decisions. Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is of great utility to generate perfusion parameter maps, but its reliance on a contrast agent with associated health risks and technical challenges limit its usability. We hypothesized that native Time-of-flight magnetic resonance angiography (TOF-MRA) can be used to generate perfusion parameter maps with an artificial intelligence (AI) method, called generative adversarial network (GAN), offering a contrast-free alternative to DSC-MRI.MethodsWe propose an adapted 3D pix2pix GAN that generates common perfusion maps from TOF-MRA images (CBF, CBV, MTT, Tmax). The models are trained on two datasets consisting of 272 patients with acute stroke and steno-occlusive disease. The performance was evaluated by the structural similarity index measure (SSIM), for the acute dataset we calculated the Dice coefficient for lesions with a time-to-maximum (Tmax) >6s.FindingsOur GAN model showed high visual overlap and high performance for all perfusion maps on both the acute stroke dataset (mean SSIM 0.88-092) and data including steno-occlusive disease patients (mean SSIM 0.83–0.98). For lesions of Tmax>6, the median Dice coefficient was 0.49.InterpretationOur study shows that our AI model can accurately generate perfusion parameter maps from TOF-MRA images, paving the way for clinical utility. We present a non-invasive alternative to contrast agent-based imaging for the assessment of cerebral hemodynamics in patients with cerebrovascular disease. Leveraging TOF-MRA data for the generation of perfusion maps represents a groundbreaking approach in cerebrovascular disease imaging. This method could greatly impact the stratification of patients with cerebrovascular diseases by providing an alternative to contrast agent-based perfusion assessment.FundingThis work has received funding from the European Commission (Horizon2020 grant: PRECISE4Q No. 777107, coordinator: DF) and the German Federal Ministry of Education and Research (Go-Bio grant: PREDICTioN2020 No. 031B0154 lead: DF).

Publisher

Cold Spring Harbor Laboratory

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3