Cerebrovascular Reserve Imaging with Simultaneous PET/MRI Using Arterial Spin Labeling and Deep Learning

使用动脉自旋标记和深度学习同时进行 PET/MRI 脑血管储备成像

基本信息

批准号：
10205063
负责人：
Gregory George Zaharchuk
金额：
$ 65.71万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-20 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10205063
关键词：
Acetazolamide Address Age Artificial Intelligence Biological Blood flow Brain Brain imaging Bypass Cardiac Cardiology Carotid Stenosis Cause of Death Cerebrovascular Circulation Cerebrovascular Disorders Clinical Clinical Trials Design Computer Vision Systems Consensus Cytolysis Data Deposition Diagnostic Excision Gadolinium Gender Goals Gold Guidelines ImProv Image Imaging problem Impairment Individual Intervention Intervention Trial Japanese Population Label Link Machine Learning Magnetic Resonance Imaging Measurement Measures Meta-Analysis Methodology Methods Modeling Motion Noise Nutrient Outcome Oxygen Patient Triage Patients Physiologic pulse Physiological Play Positron-Emission Tomography Premenopause Procedures Radiation Reference Standards Reproducibility Rest Risk Role Scanning Signal Transduction Solid Spin Labels Stress Tests Stroke prevention Techniques Testing Time Training United States Validation Waste Products Water Woman Xenon base blood flow measurement cerebral hemodynamics cerebrovascular cerebrovascular imaging clinical application cohort convolutional neural network deep field survey deep learning design direct application disability evidence based guidelines gender difference imaging modality improved learning network men nervous system disorder novel strategies performance tests prospective response stressor stroke risk systematic review time use tool

项目摘要

Cerebrovascular disease remains a common cause of death and major disability in the United States, and identifying and preventing strokes should be a high priority. Direct measurement of regional cerebral blood flow (CBF) is challeng- ing in these patients, since we do not have a non-invasive, radiation-free imaging method that has been appropriately validated against gold standard techniques. This is important, because there is compelling evidence that measuring the CBF change before and after a stress test meant to increase CBF (a measurement known as cerebrovascular reserve [CVR]) can identify patients at increased stroke risk. Stress tests have been a mainstay of the diagnostic workup of cardiology patients for many years, and we believe strongly that their use will benefit cerebrovascular disease patients as well. The goal of this project is to improve the quality of arterial spin label (ASL) MRI using deep learning, a powerful form of machine learning, that is currently undergoing tremendous progress. We will then to apply this in a prospective, adaptive validation trial against oxygen-15 water PET CBF, using simultaneous PET/MRI to minimize biological variability. Finally, we will apply this improv- ed tool to study the effects of gender on CVR and its reproducibility. Successful completion of this study will result in a validated methodology to assess CVR in cerebrovascular disease patients without the use of radiation or contrast. As such, it will provide solid, evidence-based recommendations for clinicians developing new paradigms and interventions in patients with impaired CVR.

脑血管疾病仍然是死亡和重大残疾的常见原因在美国，识别和预防中风应该是一项高度重视的工作优先事项。直接测量局部脑血流量 (CBF) 具有挑战性对这些患者进行治疗，因为我们没有无创、无辐射的治疗方法已根据金标准进行适当验证的成像方法技术。这很重要，因为有令人信服的证据表明测量旨在增加 CBF 的压力测试前后的 CBF 变化（一种称为脑血管储备 [CVR] 的测量方法）可以识别患者中风风险增加。压力测试一直是诊断的主要手段对心脏病患者进行了多年的检查，我们坚信他们的使用也将使脑血管疾病患者受益。该项目的目标是提高动脉旋转标签（ASL）的质量使用深度学习的 MRI，深度学习是机器学习的一种强大形式，目前正在取得巨大的进步。然后我们将把它应用到未来，针对氧 15 水 PET CBF 的自适应验证试验，使用同时 PET/MRI 可最大限度地减少生物变异性。最后，我们将应用这个改进 ed 工具来研究性别对 CVR 及其再现性的影响。成功完成这项研究将产生一种经过验证的方法在不使用放射或治疗的情况下评估脑血管疾病患者的 CVR 对比。因此，它将为以下方面提供可靠的、基于证据的建议：临床医生为患者开发新的范例和干预措施 CVR 受损。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

18F-FSPG PET/CT Imaging of System xC- Transporter Activity in Patients with Primary and Metastatic Brain Tumors.

原发性和转移性脑肿瘤患者中系统 xC-转运蛋白活性的 18F-FSPG PET/CT 成像。

DOI：
发表时间：
2022-06
期刊：
影响因子：
19.7
作者：
Wardak, Mirwais;Sonni, Ida;Fan, Audrey P;Minamimoto, Ryogo;Jamali, Mehran;Hatami, Negin;Zaharchuk, Greg;Fischbein, Nancy;Nagpal, Seema;Li, Gordon;Koglin, Norman;Berndt, Mathias;Bullich, Santiago;Stephens, Andrew W;Dinkelborg, Ludger M;Abel
通讯作者：
Abel

Arterial Transit Awesomeness.

动脉运输的惊人效果。

DOI：
10.1148/radiol.2020203838
发表时间：
2020-10-13
期刊：
Radiology
影响因子：
19.7
作者：
G. Zaharchuk
通讯作者：
G. Zaharchuk

Applications of Deep Learning to Neuro-Imaging Techniques.

深度学习在神经成像技术中的应用。

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
Zhu, Guangming;Jiang, Bin;Tong, Liz;Xie, Yuan;Zaharchuk, Greg;Wintermark, Ma
通讯作者：
Wintermark, Ma

Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

使用深度学习的混合 PET/MR 和 PET/CT 成像的下一代研究应用。

DOI：
发表时间：
2019-12
期刊：
影响因子：
9.1
作者：
Zaharchuk; Greg
通讯作者：
Greg

Cerebral blood flow, transit time, and apparent diffusion coefficient in moyamoya disease before and after acetazolamide.

烟雾病在乙酰唑胺治疗前后的脑血流、通过时间和表观扩散系数。

DOI：
发表时间：
2017-01
期刊：
影响因子：
2.8
作者：
Federau, Christian;Christensen, Soren;Zun, Zungho;Park, Sun;Ni, Wendy;Moseley, Michael;Zaharchuk, Greg
通讯作者：
Zaharchuk, Greg