Sparsity-Based MRI Reconstruction of Physiologic Dimensions

 描述（由申请人提供）：虽然磁共振成像（MRI）在临床上非常有价值，但是当前的成像方法在存在生理（例如，呼吸和心脏）运动以及心律失常，从而限制了MRI在许多患者中的实际应用。目前使用的MRI方法在研究自由呼吸和心律失常对心脏的影响方面也受到限制。拟议中的研究将进一步开发和评估一种新的方法来成像的存在下，生理运动，参数化这样的运动与一个变量，被视为一个额外的“维度”进行重建。由于需要额外的相关数据采集，这在传统方法中实际上不可行。然而，通过使用基于稀疏性的图像重建方法，图像沿着这些附加维度的高度相关性允许良好质量的图像重建，即使是严重欠采样的成像数据。我们已经成功地初步实现了这种新方法的2D电影成像和3D MR血管造影。在拟议的研究中，我们将进一步改进这些初始实现，并将其扩展到包括我们的方法用于其他MRI序列的实现，特别是全3D电影数据采集。我们将评估这些新方法在自由呼吸和心律失常的情况下与传统临床成像方法相比可实现的图像质量，使用数值体模模拟和儿科患者的临床心脏功能分析来测试性能。我们还将评估从这些多维图像集中提取新的功能信息的潜力，包括自由呼吸和心律失常对心脏的影响，使用我们将开发的分析工具。如果这项研究成功，这些新方法将在自由呼吸和心律失常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信息。它们还可以用于在运动的情况下进行MRI，这对于心血管和肌肉骨骼应用都很有用，以及与其他类型的成像结合，例如与集成的PET/MRI系统结合。因此，这项研究应该进一步增加MR成像对许多患者的临床实用性。

 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.