Faster Dynamic MRI with Sparse Sampling

描述（由申请人提供）：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，由于现有技术的成像速度有限，尚未充分用于临床应用。自MRI发明以来，对更高成像速度的追求一直是MRI研究的主要驱动力。尽管在过去的三十年里，快速MRI技术取得了巨大的进步，但几乎所有的MRI应用都可以从额外的加速中受益，许多潜在的应用只有在显著加速的情况下才有可能实现。拟议项目的主要目标是通过利用稀疏采样理论的最新突破和PI组在该领域的新工作，产生更快的MRI技术。 这一目标将通过以下方面的具体研究努力来实现：a）开发和优化一种新的方法，用于使用部分可分离性和空间-频谱约束从高度欠采样的（k，t）空间数据重建图像，B）分析和表征所提出的方法的分辨率和噪声特性，以及c）使用体模和大鼠研究来评估和验证所提出的用于心脏成像应用的方法。 研究工作的成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性;该框架将实现稀疏数据采样，并显着加速当前的MRI方法。其次，它将产生新的MR成像技术，提高现有MRI系统的性能，并提供一种新的方法来优化当前和下一代MRI系统中MR数据采集和图像重建的设计。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).