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，以实现未来更大的临床应用。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，以产生新的快速获取方法。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，这些数据可以使用称为GRAPPA的平行成像方法的特殊配方进行重建，以重建无差错的图像。利用这种技术，我们获得了时间分辨率为60ms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，我们相信将有可能在不到40ms的时间内生成图像，这将允许实时获取自由呼吸的心脏图像，使心功能检查无需心电图门控和屏气。为了使这些重建在临床可接受的时间范围内成为可能，它们将在GPU平台上实现，这将把重建时间从几分钟缩短到几秒钟。在项目的独立阶段，将利用GPU平台来研究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，包括压缩感知在内的这些技术也已成为可能的快速成像方法的一个新的重要类别。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。这些方法的力量是显而易见的，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受限重建方法的潜力，并研究将它们与早期开发的非笛卡尔平行成像方法相结合的可能性。快速计算平台，以GPU实现的形式，将允许这些新的图像重建技术得到有力的测试，为这些方法成为广泛的临床应用铺平道路。

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.