RespirAct Synchronization with MRI and its Impact on CVR Mapping: A Simultaneous Gas Control-Imaging System

RespirAct 与 MRI 同步及其对 CVR 映射的影响：同步气体控制成像系统

• 批准号: 556609-2020
• 负责人: Khamesee, MirBehrad
• 金额: $ 1.51万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706429
• 关键词: RespirAct; Synchronization; MRI; its; Impact

Inhalation of a vasoactive stimulus such as carbon dioxide (CO2) results in changes in cerebral blood flow (CBF), which may be measured using various techniques such as Positron Emission Tomography, Transcranial Doppler, and Magnetic Resonance Imaging (MRI). Among these various techniques, blood oxygen level dependent (BOLD)-MRI is preferred due to its non-invasiveness and excellent spatial resolution. The change of the BOLD signal in response to CO2 is mapped over the whole brain as cerebrovascular reactivity (CVR) to aid in the diagnose and treatment in cerebrovascular disease. Moreover, such CVR mapping can be utilized for arterial steno-occlusive and neurodegenerative disease. Controlling the vasoactive CO2 stimulus and measuring the BOLD signal using MRI are fundamental for determining CVR. The change in BOLD is measured from the magnetic resonance signal, and the CO2 stimulus is carefully controlled in order to accurately measure CVR. In this proposal a synchronize mechanism and a machine learning model will be developed in order to enhance the CVR mapping quality. The proposal is defined as a two-year master's research project at the University of Waterloo where all the students benefit of having equal right to access academic resources and support. This project mainly focused on six objectives: 1) Magnetic and electrical structural modeling of the MRI and the RespirAct systems, 2) control system design, 3) experiment, 4) Image Processing algorithms, 5) CVR atlas production, and 6) publication. Furthermore, as a long-term objective it is worth mentioning that this project can be integrated with another NSERC funded project of Maglev Laboratory, wireless electromagnetic actuation, in order to provide non invasive surgery for the patients. During the course of the project a graduate student will be involved. The student will be trained in a number of key areas, including mechatronics, electromagnetic system design, finite element modeling, prototyping and manufacturing, Image Processing, and Machine Learning algorithms.

吸入血管活性刺激（例如二氧化碳（CO2））会导致脑血流变化（CBF），可以使用各种技术（例如正电子发射断层扫描，经颅多普勒和磁共振成像（MRI））进行测量。 在这些各种技术中，由于其非侵入性和出色的空间分辨率，因此首选血氧水平（BOLD）-MRI。 响应于二氧化碳的大脑的粗体信号的变化被映射在整个大脑中，作为脑血管反应性（CVR），以帮助脑血管疾病的诊断和治疗。此外，此类CVR映射可用于动脉steno coccly和神经退行性疾病。 控制血管活性二氧化碳刺激并使用MRI测量粗体信号是确定CVR的基础。大胆的变化是根据磁共振信号测量的，并且仔细控制了CO2刺激以准确测量CVR。 在此提案中，将开发同步机制和机器学习模型，以增强CVR映射质量。该提案被定义为滑铁卢大学为期两年的硕士研究项目，所有学生都受益于获得学术资源和支持的平等权利。 该项目主要集中在六个目标上：1）MRI和呼吸系统系统的磁性和电气结构建模，2）控制系统设计，3）实验，4）图像处理算法，5）CVR ATLAS的产生和6）出版物。此外，作为一个长期目标，值得一提的是，该项目可以与另一个由NSERC资助的实验室，无线电磁驱动的项目集成，以便为患者提供非侵入性手术。 在项目过程中，研究生将参与其中。该学生将在许多关键领域进行培训，包括机电一体化，电磁系统设计，有限元建模，原型和制造，图像处理以及机器学习算法。