Novel Fast Imaging and Reconstruction Strategies for Dynamic MRI

摘要 这项研究项目的目标是开发新的、超快速的动态方法 成像应用，以在未来实现更大的临床实用。我们打算实现这一目标 通过结合现有的几种图像重建方法，即并行成像和 非笛卡尔轨迹，生成新的快速捕获方法。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，以 在非常短的时间内获得高度加速的数据集。然后，这些数据可以 使用一种称为GRAPPA的并行成像方法的特殊公式进行重建 以重建无错误的图像。使用这项技术，我们已经获得了具有 时间分辨率为60ms。我们计划将这一概念扩展到具有 甚至有可能获得更快的数据，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，这将允许获取实时、自由呼吸的心脏图像， 使心功能检查不再需要EKG门控和屏气。为了 使这些重建在临床可接受的时间框架内成为可能，它们将是 在GPU平台上实现，将重建时间从几分钟减少到 几秒钟。 在项目的独立阶段，将开发GPU平台，以便 研究了不同的MRI数据约束重建方法。除了并行之外 成像和非笛卡尔采集，这些技术包括压缩 传感也已经成为一种可能的快速成像的新的重要类别 方法：研究方法。早期的研究表明，数据减少了多达20%，因此， 需要一张图片。这些方法的威力是显而易见的，尽管目前还不清楚 例如，由于计算时间长得难以置信，它们在临床环境中是可行的 (有时长达数天)。因此，根据我们在这项建议的第一阶段的经验， 该项目的独立部分将探索这些限制的潜力 重建方法，并研究将它们与非 笛卡尔并行成像方法是在早期发展起来的。快速计算 平台，以GPU的形式实现，将允许这些新颖的图像重建 要大力测试技术，为这些方法变得实用铺平道路 广泛的临床应用。

Abstract 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 breathholding 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.