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.

项目总结/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，称为定量转运 映射（QTM）。MRI和医学成像中的当前/传统灌注量化通常基于 凯蒂的方法，假设相同的动脉输入到成像体积中的所有体素中。这一全球 动脉输入功能（AIF）违反体素的局部质量守恒，并要求用户 选择动脉感兴趣区域（ROI），其结果灌注值高度依赖于ROI 这就是所谓的AIF问题。 被 凯蒂的灌注定量方法中的AIF问题 这是阻碍MRI灌注定量的一个主要挑战。 需要输入体素来解决AIF问题，并且可以通过跟踪示踪剂传输来估计 根据流体力学通过血管空间，这是所提出的QTM。因此，我们建议 开发用于MRI灌注定量的QTM技术，能够处理所有3种主要类型的图像： 动态磁敏感对比（DSC）作为缺血性卒中的成像，多延迟动脉自旋标记（ASL）作为 在成像肾移植，和动态对比增强（DCE）成像乳腺肿瘤。我们计划 通过以下三个具体目标实现这一目标： 目的 包括 目的 西姆弗斯 示踪剂 目的 处理 在 灌注 1发展血管类Simverse对于大脑、肾脏和乳房，我们将发展血管类Simverse 脉管系统、流量和渗透率分布以及示踪剂传播的数据集。 2绘制分区定量运输图。血管中的数据集 用于训练DNN以用于脉管系统、流动和渗透性的QTM确定，以及 追踪时空图像的传播 3评价患者的定量灌注标测。对开发的QTM进行评价， 三种主要的灌注MRI采集：DSC、多延迟ASL和DCE。 总之，该项目的成功结果将建立基于流体力学的QTM， 定量 . D 作为保留局部质量的凯蒂方法的更有效的替代方案 不存在AIF问题。