Improved cardiac and vascular MRI using parallel imaging and compressed sensing

描述(申请人提供)：这项建议的目标是通过将非笛卡尔并行成像技术的新概念与新出现的压缩采样理论相结合，在心脏和血管成像的成像速度和信噪比方面取得新的进展。压缩感知通过打破成像时间和信噪比之间的传统联系，有望给核磁共振领域带来革命性的变化。在这里，我们将利用这些概念来开发一套全新的成像策略，大大提高信噪比和成像速度。我们通过为高端图形处理单元开发开放源码软件发行版来专门解决计算限制。这些处理器承诺在医学成像中全面减少计算时间。最终，我们相信，从整体上看，这些技术将导致一类新的心脏和血管诊断方法，这些方法将提高MRI的图像质量、信噪比和速度，这可能是MRI发展过程中无与伦比的，从而显著改善MR血管成像、心脏功能和心脏灌注。我们的具体目标是：1)开发和评估用于2D MRI应用的多层非笛卡尔并行成像方法的改进方法和评估2)开发和评估稳健的组合非笛卡尔并行成像和压缩传感方法3)开发和评估基于图形处理单元(GPU)的计算非笛卡尔并行成像的改进计算方法、CG-HYPR及其组合方法以获得临床可接受的重建时间和4)验证并行CG-HYPR方法用于心血管疾病评估作为一种缩短总检查时间和提高图像质量的手段。

DESCRIPTION (provided by applicant): The objective of this proposal is to produce a new level of gains in imaging speed and SNR for cardiac and vascular imaging by combining novel concepts of non-Cartesian parallel imaging techniques with the newly emerging compressed sampling theory. Compressed sensing promises to revolutionize the field of MRI by breaking the traditional link between imaging time and SNR. Here we will exploit these concepts to develop a set of completely new imaging strategies with dramatic increases in SNR and imaging speed. We specifically address computational limitations by developing an open source software distribution for high-end graphical processing units. These processors promise to dramatically reduce computational time across the board in medical imaging. Ultimately we believe that these technologies, when viewed as a whole, will result in a novel class of methods for cardiac and vascular diagnosis which will provide an increase in image quality, SNR and speed in MRI, perhaps unparalleled in the evolution of MRI, resulting in dramatically improved imaging of MR angiography, cardiac function and cardiac perfusion. Our specific aims are to: 1) develop and evaluate improved methods to acquire, and reconstruct multislice non-Cartesian parallel imaging methods for 2D MRI applications 2) develop and evaluate robust combined non-Cartesian parallel imaging and compressed sensing methods 3) develop and evaluate improved computational methods based on graphical processing units (GPUs) for the calculation of non-Cartesian parallel imaging, CG-HYPR and combined methods to achieve clinically acceptable reconstruction times and 4) validate parallel CG-HYPR methods for the evaluation of cardiovascular disease as a means to shorten total exam time and increase image quality.