Image acquisition and analysis tools for magnetic resonance imaging near brain injury

在接受过手术或脑损伤的患者中，在损伤周围区域获得的磁共振成像（MRI）数据可能会出现扭曲或伪影。在核磁共振成像中，射频电磁脉冲被用来激发样本中的信号，而以千赫兹频率切换的电磁场梯度脉冲被用来告诉我们样本中信号来自何处，从而创建图像。把这两种形式的电磁脉冲结合起来，就形成了一种叫做脉冲序列的工具。在获取MRI数据后，可以重建详细的图像。可以创建新的脉冲序列，以最大限度地减少伪影，并从样品中提取独特的信息。数据采集后，可以开发模型和图像处理工具，将多个脉冲序列获得的信息结合起来，提取特征并隔离单个成像序列获得的数据中不可见的结构。

In patients who have had surgery or brain injuries there may be distortions or artifacts in the magnetic resonance imaging (MRI) data acquired in the region surrounding the injury. In MRI, radio frequency electromagnetic pulses are used to excite a signal from the sample and electromagnetic field gradient pulses, switched at kilohertz frequencies are used to tell us where in the sample that signal is coming from allowing the creation of an image. Combined, these two forms of electromagnetic pulses create tools called pulse sequences. After the acquisition of MRI data, a detailed image can be reconstructed. Novel pulse sequences can be created to minimize artifacts and extract unique information from the sample. After data acquisition, it is possible to develop models and image processing tools which combine the information obtained by multiple pulse sequences to extract features and isolate structures not visible in the data obtained from a single imaging sequence. This program focuses two areas of MR physics research 1) Investigating and developing pulse sequences which are capable of minimizing artifacts and distortions; and 2) Developing and testing image-analysis tools which may aid in the visualization of regions of the brain that are typically difficult to analyze. Pulse Sequence: My research aims to investigate new pulse sequence designs which can minimize technical challenges and facilitate improved MRI in environments typically corrupted by artifacts. The goal of this work is to address imaging limitations through the development of new pulse sequences that couple accelerated data collection strategies, which allow us to acquire from multiple regions of the sample simultaneously, and optimized signal excitation and acquisition strategies, which allow us to minimize delay-induced image distortions. Image processing methodology: Most structural segmentation tools rely on existing brain atlases as well as imaging sequences whose contrasts may be changed by the presence of unexpected materials. We will create tools which improve segmentation and are capable of combining information from different MRI sequences, including novel ones under development. We will carefully question and test our models and results to reduce or eliminate bias and use the results of our analysis to inform pulse sequence design. Through this work I intend to train a diverse group of graduate and undergraduate students. I will leverage my experience in science outreach to supplement passive and departmental recruitment by actively encourage candidates from a range of backgrounds. The ability to effectively evaluate samples which are traditionally difficult to image due to artifacts will extend the utility of this powerful tool to include patients who would otherwise be unable to access informative MRI exams.