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.

吸入二氧化碳(CO2)等血管活性刺激会导致脑血流(CBF)的变化，这可以使用正电子发射断层扫描、经颅多普勒和磁共振成像(MRI)等各种技术来测量。在这些技术中，血氧水平依赖(BOLD)-MRI因其无创性和良好的空间分辨率而成为首选。BOLD信号对二氧化碳响应的变化被映射到整个大脑作为脑血管反应性(CVR)，以辅助脑血管疾病的诊断和治疗。此外，这种CVR标测还可用于动脉狭窄闭塞和神经退行性疾病。 控制血管活性二氧化碳刺激和使用MRI测量BOLD信号是确定CVR的基础。BOLD的变化是从磁共振信号测量的，二氧化碳刺激经过仔细控制，以准确测量CVR。 在该方案中，将开发一种同步机制和机器学习模型来提高CVR映射的质量。该提案被定义为滑铁卢大学为期两年的硕士研究项目，所有学生都将受益于平等获得学术资源和支持的权利。 该项目主要关注六个目标：1)MRI和RespirAct系统的磁电结构建模，2)控制系统设计，3)实验，4)图像处理算法，5)CVR图谱制作，6)出版。此外，作为一个长期目标，值得一提的是，该项目可以与NSERC资助的另一个磁悬浮实验室项目--无线电磁驱动--相结合，为患者提供无创手术。 在这个项目的过程中，将有一名研究生参与。学生将接受多个关键领域的培训，包括机电一体化、电磁系统设计、有限元建模、原型制造、图像处理和机器学习算法。