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

诸如二氧化碳（CO2）的血管活性刺激的吸入导致脑血流量（CBF）的变化，这可以使用诸如正电子发射断层扫描、经颅多普勒和磁共振成像（MRI）的各种技术来测量。 在这些各种技术中，血氧水平依赖（BOLD）-MRI由于其非侵入性和优异的空间分辨率而被优选。 BOLD信号响应CO2的变化被映射到整个大脑作为脑血管反应性（CVR），以帮助诊断和治疗脑血管疾病。此外，这种CVR标测可用于动脉狭窄闭塞和神经退行性疾病。 控制血管活性CO2刺激和使用MRI测量BOLD信号是确定CVR的基础。从磁共振信号测量BOLD的变化，并仔细控制CO2刺激，以准确测量CVR。 在这个建议中，一个同步机制和机器学习模型将被开发，以提高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，所有学生都享有平等的权利获得学术资源和支持。 该项目主要关注六个目标：1）MRI和RespirAct系统的磁和电结构建模，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，该项目可以与磁悬浮实验室的另一个NSERC资助项目无线电磁驱动相结合，以便为患者提供无创手术。 在该项目的过程中，一名研究生将参与。学生将在一些关键领域接受培训，包括机电一体化，电磁系统设计，有限元建模，原型设计和制造，图像处理和机器学习算法。

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.