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Motion-Resolved, Comprehensive Quantitative Tissue Characterization Using MR Multitasking

使用 MR 多任务处理进行运动解析、全面的定量组织表征

基本信息

批准号：
10376180
负责人：
Anthony G Christodoulou
金额：
$ 62.86万
依托单位：
CEDARS-SINAI MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10376180
关键词：
Address Aging Algorithms Arrhythmia Artificial Intelligence Blood flow Breathing Cancer Patient Cardiac Cardiovascular Diseases Cardiovascular system Clinical Collection Consumption Data Development Diagnosis Diffuse Diffusion Dimensions Disease Early Diagnosis Edema Electrocardiogram Fibrosis Hemorrhage Image Iron Joints Lead Lipids Liver Longitudinal Studies Machine Learning Magnetic Resonance Imaging Magnetism Malignant neoplasm of prostate Maps Measurement Measures Methods Modeling Monitor Morphologic artifacts Motion Nature Neurologic Organ Patients Physiological Positioning Attribute Predisposition Process Property Recovery Reproducibility Research Research Personnel Respiration Risk Assessment Scanning Series Signal Transduction Source Staging System Technology Testing Time Tissue imaging Tissues Validation body system deep learning heart motion image reconstruction magnetic field magnetohydrodynamic mathematical model multitask new technology prospective quantitative imaging reconstruction respiratory time use tissue biomarkers tool

项目摘要

PROJECT SUMMARY Quantitative magnetic resonance imaging (MRI) measures tissue parameters such as T1, T2, T2*, and diffusion to detect subtle differences in tissue states (such as microstructure, diffuse fibrosis, edema, hemorrhage, and iron content) from neurological, oncological, and cardiovascular diseases. Because each parameter offers complementary tissue information, multiparameter mapping is very promising for risk assessment, early detection, accurate staging, and treatment monitoring of disease. However, quantitative MRI is typically very time consuming and difficult to perform. Each parameter is typically measured from its own series of images, so measuring multiple parameters leads to long, inefficient scanning sessions. Furthermore, cardiac and breathing motion creates misalignment between images, causing additional problems. The standard approach to motion is to either remove it (e.g., ask the patient to hold their breath) or to synchronize image acquisition with it (e.g., using electrocardiography (ECG) to monitor cardiac motion). This approach makes scan times even longer, limits imaging to patients who can repeatedly perform long breath holds (which is difficult for aging or weak patients) and who have predictable cardiac motion (which is not true of patients with cardiac arrhythmias). Furthermore, these methods are often unreliable and difficult to perform. This project is to develop and validate a new technology, MR Multitasking, to perform multiple simultaneous measurements in a single, push-button scan that is both comfortable for patients and simple for technologists to perform. MR Multitasking redesigns quantitative MRI around the concept of images as functions of many time dimensions, each corresponding to a different dynamic process (e.g., motion, T1, T2, T2*, and diffusion), and then uses mathematical models called low-rank tensors to perform fast, multidimensional imaging. This allows continuous acquisition of imaging data even while the subject is moving, providing motion-resolved parameter maps without breath holding or motion synchronization. We will scan healthy subjects, liver patients, prostate cancer patients, and cardiovascular patients to develop and validate this technology and use artificial intelligence to quickly reconstruct images from the collected data. The resulting tool will be applicable to any organ system, offering clinicians and investigators a valuable tool to answer a wide range of biomedical questions.

项目概要定量磁共振成像 (MRI) 可测量组织参数，例如 T1、T2、T2* 和扩散以检测组织状态的细微差异（例如微观结构、弥漫性纤维化、水肿、神经、肿瘤和心血管疾病引起的出血和铁含量）。因为每个参数提供补充的组织信息，多参数映射对于风险非常有希望疾病评估、早期发现、准确分期和治疗监测。然而，定量 MRI 通常非常耗时且难以执行。每个参数通常是根据其自身的测量值来测量的一系列图像，因此测量多个参数会导致扫描过程漫长且低效。此外，心脏和呼吸运动会造成图像之间的错位，从而导致其他问题。标准的运动方法是移除它（例如，要求患者屏住呼吸）或与其同步图像采集（例如，使用心电图（ECG）监测心脏运动）。这方法使扫描时间更长，将成像限制于可以重复进行长呼吸的患者保持（这对于年老或虚弱的患者来说是困难的）并且具有可预测的心脏运动（这不是真的）心律失常患者）。此外，这些方法通常不可靠并且难以执行。该项目旨在开发和验证一项新技术，即 MR 多任务处理，以执行多项任务通过单次按钮扫描同时进行测量，既让患者感到舒适，又易于操作技术人员来执行。 MR 多任务处理围绕图像概念重新设计了定量 MRI：许多时间维度的函数，每个维度对应一个不同的动态过程（例如，运动、T1、T2、 T2* 和扩散），然后使用称为低秩张量的数学模型来快速执行，多维成像。即使被摄体移动，也可以连续采集成像数据，提供运动解析参数图，无需屏气或运动同步。我们将扫描健康受试者、肝脏患者、前列腺癌患者和心血管患者进行开发和验证该技术并使用人工智能根据收集的数据快速重建图像。这由此产生的工具将适用于任何器官系统，为临床医生和研究人员提供了一个有价值的工具回答广泛的生物医学问题。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma.

DOI：
10.3389/fonc.2022.1007134
发表时间：
2022
期刊：
FRONTIERS IN ONCOLOGY
影响因子：
4.7
作者：
Wang, Nan;Gaddam, Srinivas;Xie, Yibin;Christodoulou, Anthony G.;Wu, Chaowei;Ma, Sen;Fan, Zhaoyang;Wang, Lixia;Lo, Simon;Hendifar, Andrew E.;Pandol, Stephen J.;Li, Debiao
通讯作者：
Li, Debiao

Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis.

DOI：
10.1002/mrm.28553
发表时间：
2021-04
期刊：
Magnetic resonance in medicine
影响因子：
3.3
作者：
Ma S;Wang N;Fan Z;Kaisey M;Sicotte NL;Christodoulou AG;Li D
通讯作者：
Li D

Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking.

DOI：
10.1002/mrm.28959
发表时间：
2022-01
期刊：
Magnetic resonance in medicine
影响因子：
3.3
作者：
Ma S;Wang N;Xie Y;Fan Z;Li D;Christodoulou AG
通讯作者：
Christodoulou AG

MR Multitasking-based multi-dimensional assessment of cardiovascular system (MT-MACS) with extended spatial coverage and water-fat separation.

DOI：
10.1002/mrm.29522
发表时间：
2023-04
期刊：
MAGNETIC RESONANCE IN MEDICINE
影响因子：
3.3
作者：
Hu, Zhehao;Xiao, Jiayu;Mao, Xianglun;Xie, Yibin;Kwan, Alan C.;Song, Shlee S.;Fong, Michael W.;Wilcox, Alison G.;Li, Debiao;Christodoulou, Anthony G.;Fan, Zhaoyang
通讯作者：
Fan, Zhaoyang

Data-Consistent non-Cartesian deep subspace learning for efficient dynamic MR image reconstruction.