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。

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.