Sparsity-Based MRI Reconstruction of Physiologic Dimensions

基于稀疏性的生理维度 MRI 重建

• 批准号: 8968594
• 负责人: Leon Axel
• 金额: $ 25.09万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8968594
• 关键词: Abdomen; Angiography; Area; Arrhythmia; Breathing; Cardiac; Cardiovascular Diseases; Cardiovascular system; Chest; Clinical; Data; Diagnostic; Dimensions; Disease; Evaluation; Exercise; Financial compensation; Future; Generations; Goals; Heart; Image; Imaging Phantoms; Lead; Left ventricular structure; Magnetic Resonance Imaging; Measures; Methods; Monitor; Morphologic artifacts; Motion; Musculoskeletal; Normal Range; Patients; Physiological; Positron-Emission Tomography; Property; Radial; Relative (related person); Research; Resolution; Respiration; Right ventricular structure; Sampling; Speed; Stress; Variant; Ventricular; attenuation; base; data acquisition; heart function; heart motion; image reconstruction; imaging modality; imaging system; improved; interest; musculoskeletal imaging; novel; novel strategies; pediatric patients; performance tests; practical application; public health relevance; research clinical testing; respiratory; simulation; tool

 DESCRIPTION (provided by applicant): While magnetic resonance imaging (MRI) is clinically very valuable, current imaging methods are subject to blurring and artifacts in the presence of physiologic (e.g., respiratory and cardiac) motion, as well as of arrhythmias, thus limiting the practical application of MRI in many patients. The currently used MRI methods are also limited in their ability to study the effects of free breathing and arrhythmias on the heart. The proposed research will further develop and evaluate a new approach to imaging in the presence of physiologic motion, which parameterizes such motions with a variable that is treated as an additional "dimension" to be reconstructed. This would not be practically feasible with conventional methods, due to the additional associated data acquisition that would be required. However, with the use of sparsity-based image reconstruction methods, the high degree of correlation of the images along these additional dimensions permits good quality image reconstructions, even with heavily undersampled imaging data. We already have made successful initial implementations of this new method for 2D cine imaging and 3D MR angiography. In the proposed research, we will further improve these initial implementations, and we will extend them to include implementations of our methods for other MRI sequences, particularly fully 3D cine data acquisitions. We will evaluate the image quality achievable with these new methods in the presence of free breathing and arrhythmias, as compared with conventional clinical imaging methods, using both numerical phantom simulations and clinical cardiac function analysis in pediatric patients to test the performance. We will also evaluate the potential for extracting new kinds of functional information from these multidimensional image sets, including the effects of free breathing and arrhythmias on the heart, using analysis tools that we will be developing. If this research is successful, these new methods will provide significantly improved MR image quality in the presence of free breathing and arrhythmias, as well as providing potentially valuable new kinds of information on the function of the heart. They may also be able to be used for performing MRI in the presence of exercise, which could be useful for both cardiovascular and musculoskeletal applications, as well as in combination with other kinds of imaging, such as with integrated PET/MRI systems. This research should thus further increase the clinical utility of MR imaging for many patients.

 描述（由应用提供）：虽然磁共振成像（MRI）在临床上非常有价值，但在存在生理学（例如呼吸和心脏运动）以及心律失常的情况下以及心律不齐的情况下，当前的成像方法受到模糊和伪影的影响，从而限制了许多患者在许多患者中的实际应用。当前使用的MRI方法在研究自由呼吸和心律不齐对心脏的影响的能力方面也受到限制。拟议的研究将在存在生理运动的情况下进一步开发并评估一种新的成像方法，该方法以一种变量来参数化运动，该变量被视为要重建的附加“维度”。由于需要的其他相关数据获取，因此对于常规方法而言，这实际上是不可行的。但是，通过使用基于稀疏性的图像重建方法，即使使用大量不足的成像数据，图像的高度相关性也可以允许高质量的图像重建。我们已经成功地实施了这种新方法，用于2D CINE成像和3D MR血管造影。在拟议的研究中，我们将进一步改善这些初始实现，并将其扩展到包括其他MRI序列的方法，尤其是完全3D CINE数据采集。与传统的临床成像方法相比，在存在游离呼吸和心律不齐的情况下，我们将使用这些新方法来评估这些新方法的图像质量，同时使用儿科患者中的数值模拟和临床心脏功能分析来测试表现。我们还将使用我们将要开发的分析工具来评估从这些多维图像集中提取新型功能信息的潜力，包括自由呼吸和心律不齐的影响。如果这项研究成功，这些新方法将在存在自由呼吸和心律不齐的情况下提供显着改善的MR图像质量，并提供有关心脏功能的潜在有价值的新型信息。它们也可能可以在运动的存在下用于进行MRI，这对于心血管和肌肉骨骼应用可能很有用，以及与其他类型的成像（例如集成的PET/MRI系统）结合使用。因此，这项研究应进一步增加许多患者MR成像的临床实用性。