Fluid mechanics approach to tissue perfusion quantification in MRI

MRI 中组织灌注定量的流体力学方法

• 批准号: 10720485
• 负责人: Yi Wang
• 金额: $ 66万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10720485
• 关键词: Address; Anatomy; Angiography; Arteries; Biophysics; Blood Vessels; Blood flow; Brain; Breast; Clinical Trials; Collection; Complex; Computing Methodologies; Data; Data Set; Defect; Equation; Feasibility Studies; Fingerprint; Goals; Image; Ischemic Stroke; Kidney; Kidney Transplantation; Knowledge; Laws; Liquid substance; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Neoplasms; Maps; Mechanics; Medical Imaging; Methods; Organ; Outcome; Output; Patients; Perfusion; Permeability; Positron-Emission Tomography; Predisposition; Process; Property; Quantitative Microscopy; Research Project Grants; Resolution; Specific qualifier value; Stroke; Structure; Technology; Time; Tissues; Tracer; Training; arterial spin labeling; breast imaging; clinical practice; contrast enhanced; deep learning; deep neural network; improved; interest; kidney imaging; large datasets; millisecond; preservation; simulation; tumor

PROJECT SUMMARY/ABSTRACT Our overall goal is to develop a fluid mechanics approach to studying tracer transport through tissue for perfusion quantification in magnetic resonance imaging (MRI), which is termed as quantitative transport mapping (QTM). Current/traditional perfusion quantification in MRI and medical imaging in general is based on Kety's method that assumes the same arterial input globally into all voxels in an imaging volume. This global arterial input function (AIF) transgresses the local mass conservation at a voxel and requires the user to choose an arterial region of interest (ROI) with the consequent perfusion value highly dependent on the ROI choice, which is known as the AIF problem. been The AIF problem in Kety's method for perfusion quantification has a major unmet challenge impeding perfusion quantification in MRI.The tracer concentration at the artery entering the voxel is needed to address the AIF problem and can be estimated by following tracer transport through the vascular space according to fluid mechanics, which is the proposed QTM. Accordingly, we propose to develop QTM technology for MRI perfusion quantification, capable of processing all 3 major types of images: dynamic susceptibility contrast (DSC) as in imaging ischemic stroke, multidelay arterial spin labeling (ASL) as in imaging kidney transplant, and dynamic contrast enhanced (DCE) as in imaging breast tumor. We plan to achieve this objective through the following three specific aims: Aim comprising Aim Simverse tracer Aim processing In perfusion 1 Develop vascular Simverses For the brain, kidney and breast, we will develop vascular Simverse datasets of vasculature, flow and permeability distribution, and tracer propagation. 2 Develop compartmentalized quantitative transport mapping. The datasets in the vascular of an organ are used to train DNNs for QTM determination of vasculature, flow and permeability, and propagation from tracer spacetime images. 3 Evaluate quantitative perfusion mapping in patients. The eveloped QTM is evaluated for three major perfusion MRI acquisitions: DSC, multidelay ASL and DCE. summary, the successful outcome of this project will establish the fluid mechanics based QTM for quantification . d as a more effective alternative to Kety's method with preservation of local mass conservation and without the AIF problem.

项目概要/摘要 我们的总体目标是开发一种流体力学方法来研究示踪剂通过组织的运输 磁共振成像 (MRI) 中的灌注定量，称为定量传输 映射（QTM）。 MRI 和医学成像中当前/传统的灌注量化通常基于 Kety 的方法假设成像体积中的所有体素全局都有相同的动脉输入。这个全球 动脉输入函数 (AIF) 违反了体素的局部质量守恒，需要用户 选择感兴趣的动脉区域 (ROI)，随后的灌注值高度依赖于 ROI 选择，这被称为 AIF 问题。 到过 Kety 灌注定量方法中的 AIF 问题 阻碍 MRI 灌注量化的一个主要未满足的挑战。动脉处的示踪剂浓度 需要输入体素来解决 AIF 问题，并且可以通过跟踪器传输来估计 根据流体力学穿过血管空间，这就是所提出的QTM。据此，我们建议 开发用于 MRI 灌注量化的 QTM 技术，能够处理所有 3 种主要类型的图像： 动态磁敏对比（DSC）如缺血性中风成像，多重延迟动脉自旋标记（ASL）如 肾移植成像，以及乳腺肿瘤成像中的动态对比增强 (DCE)。我们计划 通过以下三个具体目标来实现这一目标： 目的 包括 目的 模拟宇宙 示踪剂 目的 加工 在 灌注 1 开发血管Simverse 对于大脑、肾脏和乳房，我们将开发血管Simverse 脉管系统、流量和渗透性分布以及示踪剂传播的数据集。 2 开发分区定量传输图。血管中的数据集 器官的数据用于训练 DNN，以进行脉管系统、流量和渗透性的 QTM 测定，以及 从示踪时空图像传播。 3 评估患者的定量灌注图。评估开发的 QTM 三种主要的灌注 MRI 采集：DSC、多延迟 ASL 和 DCE。 总之，该项目的成功成果将为以下领域建立基于流体力学的 QTM： 量化 。 d 作为保留局部质量的凯蒂方法的更有效替代方案 保护并且没有 AIF 问题。