Faster Dynamic MRI with Sparse Sampling

描述(申请人提供)：MRI在各种疾病和生理过程的动态成像方面具有巨大的潜力，但由于现有技术成像速度有限，这一潜力尚未完全用于临床应用。自核磁共振发明以来，对更高成像速度的追求一直是核磁共振研究的主要驱动力。尽管快速磁共振成像技术在过去的三十年中取得了巨大的进步，但几乎所有的磁共振成像应用都可以从进一步的加速中受益，而且许多潜在的应用只有在显著加速的情况下才可能成为可能。拟议项目的主要目标是利用稀疏采样理论的最新突破和Pi S团队在该领域的新工作，生产出速度显著加快的磁共振成像技术。这一目标将通过具体的研究工作来实现：a)开发和优化使用部分可分离性和空间谱约束的高采样(k，t)空间数据的图像重建的新方法，b)分析和表征所提出的方法的分辨率和噪声特性，以及c)使用模型和大鼠研究来评估和验证所提出的方法用于心脏成像应用。这项研究工作的结果将在几个方面具有重要意义。首先，它将提供一个新的数学和算法框架，有效地利用多维MRI信号的稀疏性和部分可分离性；该框架将使稀疏数据采样成为可能，并显著加快当前的MRI方法。其次，它将产生新的磁共振成像技术，将增强现有MRI系统的性能，并为优化现有和下一代MRI系统中MR数据采集和图像重建的设计提供新的途径。第三，它将实现一系列具有挑战性的动态成像实验，包括实时3D心脏成像应用(例如，移植心脏的功能评估)。

DESCRIPTION (provided by applicant): MRI has enormous potential for dynamic imaging of various diseases and physiological processes, which has not been fully utilized for clinical applications due to the limited imaging speed of existing technology. The quest for higher imaging speeds has been a major driving force for MRI research since its invention. Although tremendous progress has been made in fast MRI technology over the last three decades, virtually all MRI applications could benefit from additional speedups, and many potential applications would become possible only with significant acceleration. The primary objective of the proposed project is to produce significantly faster MRI technology by leveraging the recent breakthroughs in sparse sampling theory and the novel work of the PI s group in this area. This objective will be achieved with specific research efforts on: a) developing and optimizing a novel method for image reconstruction from highly undersampled (k, t)-space data using both partial separability and spatial-spectral constraints, b) analyzing and characterizing the resolution and noise properties of the proposed methods, and c) evaluating and validating the proposed method for cardiac imaging applications using phantom and rat studies. The outcome of the research effort will be significant in several ways. First, it will provide a new mathematical and algorithmic framework that effectively exploits the sparsity and partial separability of multidimensional MRI signals; this framework will enable sparse data sampling and significantly accelerate current MRI methods. Second, it will produce new MR imaging technology that will enhance the performance of existing MRI systems and provide a new way to optimize the design of MR data acquisition and image reconstruction in current and next-generation MRI systems. Third, it will enable a range of challenging dynamic imaging experiments, including realtime 3D cardiac imaging applications (e.g., functional assessment of transplanted hearts).