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

• 批准号: 9766063
• 负责人: Anthony G Christodoulou
• 金额: $ 67.68万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766063
• 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

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.

项目摘要 定量磁共振成像（MRI）测量组织参数，例如T1、T2、T2* 和T3。 扩散以检测组织状态（例如微结构，扩散纤维化，水肿， 出血和铁含量）。因为每个 参数提供了补充的组织信息，多参数标测非常有希望用于风险 疾病的评估、早期检测、准确分期和治疗监测。然而，定量MRI 通常非常耗时并且难以执行。每个参数通常从其自身的 由于需要一系列图像，因此测量多个参数会导致扫描时间过长且效率低下。此外，委员会认为， 心脏和呼吸运动在图像之间产生不对准，从而引起额外的问题。 运动的标准方法是将其移除（例如，要求患者屏住呼吸）或 使图像采集与之同步（例如，使用心电图（ECG）来监测心脏运动）。这 这种方法使扫描时间更长，限制了可以重复进行长呼吸的患者的成像 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（这是不正确的 心律失常患者）。此外，这些方法通常不可靠且难以执行。 该项目旨在开发和验证一种新技术，MR多任务处理， 在单个按钮扫描中同时进行测量，这对患者来说既舒适又简单， 技术人员执行。MR多任务处理围绕图像概念重新设计定量MRI， 许多时间维度的函数，每个对应于不同的动态过程（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， 多维成像这允许即使在对象移动时也连续采集成像数据， 提供运动分辨的参数图而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，以开发和验证 这项技术并使用人工智能从收集的数据中快速重建图像。的 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一个有价值的工具， 回答一系列生物医学问题。