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

吸入像二氧化碳（CO2）这样的血管活性刺激物会导致脑血流量（CBF）的变化，这可以使用各种技术来测量，如正电子发射断层扫描、经颅多普勒和磁共振成像（MRI）。在这些不同的技术中，血氧水平依赖(BOLD)-MRI因其无创性和出色的空间分辨率而受到青睐。BOLD信号对CO2的响应变化作为脑血管反应性（CVR）映射到全脑，有助于脑血管疾病的诊断和治疗。此外，这种CVR制图可用于动脉狭窄闭塞性和神经退行性疾病。控制血管活性CO2刺激和利用MRI测量BOLD信号是确定CVR的基础。通过磁共振信号测量BOLD的变化，并对CO2刺激进行严格控制，以准确测量CVR。为了提高CVR的测绘质量，本文提出了一种同步机制和机器学习模型。该提案被定义为滑铁卢大学的一个为期两年的硕士研究项目，所有学生都有平等的权利获得学术资源和支持。本项目主要关注六个目标：1)MRI和呼吸器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图集制作，6)发表。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，一名研究生将参与其中。该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.