Quantitative MR Imaging of Vascular Factors in Parkinsons Disease

帕金森病血管因素的定量 MR 成像

基本信息

批准号：
10266020
负责人：
Luis Hernandez-Garcia
金额：
$ 28.1万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10266020
关键词：
Address Agreement Algorithms Alzheimer&apos s Disease Arteries Biological Biological Markers Blood Vessels Blood Volume Blood flow Bolus Infusion Brain Cerebrovascular Circulation Classification Clinic Clinical Contrast Media Development Diagnosis Disease Ensure Etiology Fatigue Fingerprint Future Health Human Volunteers Hybrids Image Injections Knowledge Label Magnetic Resonance Magnetic Resonance Imaging Maps Measurement Measures Memory Methodology Methods Modeling Movement Nerve Degeneration Neurodegenerative Disorders Neuronal Injury Noise Parkinson Disease Pathogenesis Pathologic Patients Perfusion Physiologic pulse Public Health Reproducibility Research Risk Scanning Sensitivity and Specificity Severities Signal Transduction Spatial Distribution Spin Labels Symptoms Techniques Testing Time Tissues Translating Validation Weight Work aging brain deep learning feeding hemodynamics imaging biomarker insight interest machine learning algorithm neural network non-invasive imaging novel therapeutics patient stratification perfusion imaging relating to nervous system success support vector machine therapeutic target tool vascular factor vector white matter

项目摘要

Abstract Vascular health has been shown to be an important factor in the development of neurodegenerative diseases like Alzheimer’s and Parkinson’s diseases. Hence, the ability to measure reliably and quantitatively early hemodynamic changes in the aging brain can be a powerful tool for diagnosing, studying, and developing treatments. Arterial Spin Labeling (ASL) magnetic resonance imaging can yield quantitative perfusion images without the use of contrast agents. We propose that combining new ASL techniques, such as Velocity Selective Inversion (VSI) labeling pulses and magnetic resonance fingerprinting (MRF) with deep learning regression methods will allow quantification of multiple hemodynamic parameters beyond perfusion, thus providing a much more nuanced picture of the state of the vasculature. We also expect that the new technique will offer dramatic improvements in SNR, specificity and sensitivity of ASL, and that the proposed techniques will have many other applications in research and in the clinic. We propose to use these techniques to fill the knowledge gap regarding the relationship between vascular changes and Parkinson’s disease and its symptoms, particularly fatigue, whose pathogenesis is not well understood. If we are successful in this application, future work will use the hemodynamic parameters of interest as biomarkers to assess risk of neurodegeneration, determine therapeutic targets, and guide in the development of new therapies.

抽象的血管健康已被证明是发展的重要因素神经退行性疾病，例如阿尔茨海默氏症和帕金森氏病。因此，能力可靠和定量的早期血液动力学变化的衰老大脑可以是诊断，研究和开发治疗方法的有力工具。动脉自旋标记（ASL）磁共振成像可以产生定量灌注图像不使用造影剂。我们建议结合新的ASL技术，例如速度选择性反转（VSI）标记脉冲和磁共振指纹（MRF）使用深度学习回归方法将允许量化多个血液动力学参数超出了许可，从而提供了更加细微的图片脉管系统的状态。我们还希望新技术会提供戏剧性 SNR的改善，ASL的特异性和敏感性，并提出了所提出的技术在研究和诊所中还有许多其他应用。我们建议使用这些技术来填补有关关系的知识差距血管变化和帕金森氏病及其症状，尤其是疲劳，谁发病机理尚不清楚。如果我们在此应用程序中取得成功，将来的工作将使用感兴趣的血液动力学参数是评估神经退行性风险的生物标志物，确定治疗靶标，并指导新疗法的发展。