Combined Segmentation and Hemodynamic Analysis of Cerebrovascular Structures using Spatiotemporal Arterial Spin Labeling MRI

磁共振成像（MRI）已成为不可或缺的诊断和研究工具。它能够可视化器官的解剖结构和功能。由于MRI技术的进步，脑的时间分辨三维（即4D）MRI血流测量，例如动脉自旋标记（ASL）MR血管造影（MRA），最近受到越来越多的关注。4D ASL MRA序列现在能够以优于1 mm3的空间分辨率和低于100 ms的时间分辨率对脑血管结构和血流进行成像。由于这种成像技术利用血液作为内在造影剂，因此不需要外源性造影剂，使其廉价且安全。然而，4D ASL MRA获得的大量数据限制了该技术的实用性，特别是如果仅对2D图像进行目视检查。 这项研究的主要目标是为大型时间分辨数据集开发新的图像分析算法和可视化技术，以实现血流分析、血管分割和针对4D ASL MRA数据集定制的血流动力学可视化技术，从而实现快速、准确和定量的解释。我们将设计，优化和评估各种隐式和显式模型的血流动力学分析的基础上ASL的时间-强度曲线的每个体素在第一步。脑血管系统的分割需要定量研究血管解剖结构并使结果可视化。因此，我们将开发新的先进的血管分割方法，用于从大脑的4D ASL MRA数据集中提取血管。更具体地说，我们将联合收割机的血流动力学分析和脑血管分割成一个综合的分析方法。预期这种耦合的血管分割和血流分析将导致改进的分割和血液动力学分析结果。最后，先进的可视化方法的脑血管系统及其血流的组合表示将开发和评估。例如，计划设计4D血流的基于字形的可视化以及基于动态表面的可视化技术。* * 所有的方法都将整合到一个新的软件工具中，该软件工具将提供给应用成像研究人员，从而提高4D ASL MRA的实用价值。总的来说，这项工作将实现对4D ASL MRA数据集的快速，准确和定量解释，同时创建新的图像分析和可视化方法，并广泛应用于其他图像处理问题。

Magnetic resonance imaging (MRI) has become an indispensable diagnostic and research tool. It is capable of visualizing the anatomy and function of organs. Time-resolved three-dimensional (i.e. 4D) MRI blood flow measurements, e.g. arterial spin labeling (ASL) MR angiography (MRA), of the brain is of increasing recent interest due to MRI technology advancements. 4D ASL MRA sequences are now capable of imaging cerebrovascular structures and blood flow with a spatial resolution better than 1 mm3 and a temporal resolution below 100 ms. Since this imaging technique utilizes blood as an intrinsic contrast agent, no exogenous contrast media is required, making it inexpensive and safe. However, the vast data volume obtained with 4D ASL MRA limits the utility of the technique, particularly if the only 2D images are only visually inspected. The broad goal of this research is to develop novel image analysis algorithms and visualization techniques for large time-resolved datasets to achieve blood flow analysis, vessel segmentation, and hemodynamic visualization techniques tailored to 4D ASL MRA datasets to enable fast, accurate, and quantitative interpretation.******We will design, optimize, and evaluate various implicit and explicit models for the hemodynamic analysis of blood flow dynamics based on the ASL time-intensity curves for each voxel in a first step. A segmentation of the cerebrovascular system is required to investigate the vessel anatomy quantitatively and to visualize the results. Therefore, we will develop new advanced vessel segmentation methods for extraction of the vessels from 4D ASL MRA datasets of the brain. More specifically, we will combine the hemodynamic analysis and cerebrovascular segmentation into an integrated analysis approach. It is expected that such a coupled vessel segmentation and blood flow analysis will lead to both improved segmentation and hemodynamic analysis results. Finally, advanced visualization methods for the combined representation of the cerebrovascular system and its blood flow will be developed and evaluated. For example, it is planned to design a glyph-based visualization of the 4D blood flow as well as dynamic surface-based visualization techniques. ******All methods will be integrated within a novel software tool that will be made available for applied imaging researchers, thereby enhancing the practical value of 4D ASL MRA. Collectively, this work will enable a fast, accurate, and quantitative interpretation of 4D ASL MRA datasets while creating new image analysis and visualization methods with broad applications for other image processing problems.