Novel Fast Imaging and Reconstruction Strategies for Dynamic MRI

描述（由申请人提供）：本研究项目的目标是为动态成像应用开发新的超快速方法，以在未来实现更大的临床实用性。我们打算通过结合几种现有的图像重建方法，即并行成像和非笛卡尔轨迹，来实现这一目标，以产生新的快速采集方法。我们目前的研究涉及使用径向轨迹，而不是标准的直线轨迹，在很短的时间内获得高度加速的数据集。然后，这些数据可以使用被称为GRAPPA的并行成像方法的特殊配方进行重建，以重建无误差图像。使用这种技术，我们已经获得了60毫秒的时间分辨率的图像。我们计划将这一概念扩展到具有快速数据采集潜力的轨迹，即螺旋和各向异性视场轨迹。使用这些方法，我们相信有可能在不到40 ms的时间内生成图像，这将允许采集实时、自由呼吸的心脏图像，使EKG门控和屏气对于心脏功能检查是不必要的。为了使这些重建在临床上可接受的时间范围内成为可能，它们将在GPU平台上实现，这将使重建时间从几分钟减少到几秒。 在项目的独立阶段，将利用GPU平台研究MRI数据的不同约束重建方法。除了并行成像和非笛卡尔采集之外，包括压缩传感的这些技术也已经成为可能的快速成像方法的新的重要类别。早期的工作已经证明，数据减少了20倍，因此图像所需的时间减少了20倍。这些方法的威力是显而易见的，尽管目前还不清楚它们在临床环境中是否可行，例如，由于令人难以置信的长计算时间（有时长达几天）。因此，根据我们在本提案第一阶段的经验，本项目的独立部分将探索这些约束重建方法的潜力，并研究将其与早期开发的非笛卡尔并行成像方法相结合的可能性。以GPU实现形式的快速计算平台将允许这些新颖的图像重建技术得到有力的测试，为这些方法在临床上的广泛应用铺平道路。 公共卫生相关性：虽然磁共振成像（MRI）由于其对广泛范围的疾病的敏感性而在临床上被广泛使用，但是MRI数据的相对缓慢的采集限制了其对许多动态成像情况（诸如心脏成像或MR血管造影）的适用性。该项目的目标是开发图像重建技术，用于超快速MRI成像，使用新的采集和信号处理方法的组合。使用这些技术的GPU实现的快速计算将允许重建在几秒钟内发生，从而允许该技术在临床环境中实施。这些方法将彻底改变动态MRI数据的采集和重建。

DESCRIPTION (provided by applicant): The goal of this research project is to develop new, ultra-fast methods for dynamic imaging applications to enable greater clinical utility in the future. We intend to meet this goal by combining several existing image reconstruction methods, namely parallel imaging and non-Cartesian trajectories, to generate novel fast acquisition methods. Our current research involves the use of radial trajectories, as opposed to the standard, rectilinear trajectory, to acquire highly accelerated datasets in a very short time. These data can then be reconstructed using a special formulation of a parallel imaging method known as GRAPPA in order to reconstruct error-free images. Using this technique, we have acquired images with a temporal resolution of 60ms. We plan to expand this concept to trajectories which have the potential for even fast data acquisition, namely spiral and anisotropic field-of-view trajectories. Using these methods, we believe that it will be possible to generate images in less than 40ms, which will allow the acquisition real-time, free-breathing cardiac images, making EKG gating and breath holding unnecessary for cardiac function exams. In order to make these reconstructions possible in a clinically acceptable timeframe, they will be implemented on a GPU platform, which will reduce the reconstruction time from minutes to seconds. In the independent phase of the project, the GPU platform will be exploited in order to investigate different constrained reconstruction methods for MRI data. In addition to parallel imaging and non-Cartesian acquisitions, these techniques which include compressed sensing have also emerged as a new and important category of possible fast imaging methods. Early work has demonstrated an up to 20-fold reduction in data, and thus time, needed for an image. The power of these methods is obvious, although it is not yet clear if they will be viable in a clinical setting, due to, for instance, incredibly long computation times (sometimes up to days). Thus based on our experience in the first stage of this proposal, the independent portion of this project will explore the potential of these constrained reconstruction methods and examines the possibility of combining them with the non- Cartesian parallel imaging methods developed in the earlier phase. The rapid computational platform, in the form of the GPU implementations, will allow these novel image reconstruction techniques to be vigorously tested, paving the way for these methods to become practical for widespread clinical use. PUBLIC HEALTH RELEVANCE: While magnetic resonance imaging (MRI) is in widespread clinical use because of its sensitivity to a broad range of diseases, the relatively slow acquisition of MRI data limits its applicability to many dynamic imaging situations such as cardiac imaging or MR angiography. The goal of this project is to develop image reconstruction techniques for ultra-fast MRI imaging using a combination of novel acquisition and signal processing methods. Rapid computing using GPU implementations of these techniques will allow the reconstructions to take place in a matter of seconds, allowing this technology to be implemented in a clinical setting. These methods will revolutionize the acquisition and reconstruction of dynamic MRI data.