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

使用时空动脉自旋标记 MRI 进行脑血管结构的组合分割和血流动力学分析

• 批准号: RGPIN-2016-04068
• 负责人: Forkert, NilsDaniel
• 金额: $ 5.25万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764452
• 关键词: Combined; Segmentation; Hemodynamic; Analysis; Cerebrovascular

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.

磁共振成像(MRI)已成为一种不可或缺的诊断和研究工具。它能够可视化器官的解剖和功能。近年来，由于MRI技术的进步，脑的时间分辨三维(即4D)MRI血流测量，如动脉自旋标记(ASL)MR血管成像(MRA)越来越受到人们的关注。目前，4D ASL MRA序列能够成像脑血管结构和血流，空间分辨率优于1mm3，时间分辨率低于100ms。由于这种成像技术使用血液作为内部造影剂，因此不需要外源性造影剂，因此价格低廉且安全。然而，4D ASL MRA获得的巨大数据量限制了该技术的应用，特别是在仅对2D图像进行视觉检查的情况下。本研究的主要目标是为大数据集开发新的图像分析算法和可视化技术，以实现针对4D ASL MRA数据集的血流分析、血管分割和血流动力学可视化技术，从而实现快速、准确和定量的解释。首先，我们将基于每个体素的ASL时间强度曲线设计、优化和评估各种用于血流动力学分析的隐式和显式模型。需要对脑血管系统进行分割，以定量研究血管解剖并将结果可视化。因此，我们将开发新的先进的血管分割方法，用于从4D ASL脑部MRA数据集中提取血管。更具体地说，我们将把血流动力学分析和脑血管分割结合成一个综合的分析方法。预计这样的血管分割和血流分析耦合将导致更好的分割和血流动力学分析结果。最后，将开发和评估用于组合表示脑血管系统及其血液流动的高级可视化方法。例如，它计划设计一种基于字形的4D血流可视化以及基于动态表面的可视化技术。所有方法都将集成到一个新的软件工具中，供应用成像研究人员使用，从而增强4D ASL MRA的实用价值。总而言之，这项工作将使4D ASL MRA数据集的快速、准确和定量解释成为可能，同时创建新的图像分析和可视化方法，为其他图像处理问题提供广泛的应用。