Faster Dynamic MRI with Sparse Sampling

稀疏采样的更快动态 MRI

• 批准号: 8531928
• 负责人: ZHI-PEI LIANG
• 金额: $ 32.8万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8531928
• 关键词: Acceleration; Angiography; Area; Beds; Cardiac; Clinical; Computer Simulation; Data; Detection; Disease; EFRAC; Functional Imaging; Goals; Heart Transplantation; Image; Imaging Techniques; Imaging technology; Lung; Magnetic Resonance Imaging; Measurement; Methods; Modeling; Myocardial perfusion; Noise; Outcomes Research; Performance; Perfusion; Physiological; Physiological Processes; Process; Property; Rattus; Research; Resolution; Sampling; Scanning; Scheme; Signal Transduction; Speed; System; Technology; Testing; Theoretical Studies; Time; Universities; Validation; Work; base; cancer imaging; clinical application; data acquisition; data space; design; disease diagnosis; driving force; image reconstruction; imaging modality; method development; neuroimaging; new technology; next generation; novel; reconstruction; research study; respiratory; spatiotemporal; theories

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).

描述（由申请人提供）：MRI具有各种疾病和生理过程动态成像的巨大潜力，由于现有技术的成像速度有限，因此尚未将其全面用于临床应用。自发明以来，对更高的成像速度的追求一直是MRI研究的主要推动力。尽管在过去的三十年中，在快速MRI技术中取得了巨大进展，但实际上，所有MRI应用程序都可以从额外的速度中受益，并且只有通过大量加速，许多潜在的应用才能成为可能。该项目的主要目的是通过利用稀疏抽样理论的最新突破以及PI S组在该领域的新工作来生产更快的MRI技术。 通过针对以下特定的研究工作，将实现这一目标：a）使用部分可分离性和空间 - 光谱约束来开发和优化一种新的图像重建方法（K，T）空间数据，b）分析和表征所提出方法的分辨率和噪声特性，以及使用ph进行ph和cantiak for cardiaS for cardiac for cartiak for cardiac for cartiak for cardiac for cartist cations for cardiac for cartist and cating catiging catiging catiging catiging cating and catiging catiging cating and cating and Imaging cating and Imaging and Imageomes。 研究工作的结果在几种方面将是重要的。首先，它将提供一个新的数学和算法框架，可有效利用多维MRI信号的稀疏性和部分可分离性；该框架将实现稀疏的数据采样，并显着加速当前的MRI方法。其次，它将生产新的MR成像技术，以增强现有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，对移植心脏的功能评估）。