Improved cardiac and vascular MRI using parallel imaging and compressed sensing

描述（由申请人提供）：本提案的目标是通过将非笛卡尔平行成像技术的新概念与新出现的压缩采样理论相结合，为心脏和血管成像提供新的成像速度和信噪比。压缩感知通过打破成像时间和信噪比之间的传统联系，有望彻底改变MRI领域。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。我们专门通过开发高端图形处理单元的开源软件发行版来解决计算限制问题。这些处理器有望大幅减少医学成像领域的计算时间。最终，我们相信这些技术，当作为一个整体来看，将导致心脏和血管诊断的一种新型方法，这将提供MRI图像质量，信噪比和速度的提高，这在MRI的发展中可能是无与伦比的，从而显着改善MR血管造影，心功能和心脏灌注成像。我们的具体目标是：1)开发和评估用于二维MRI应用的获取和重建多层非笛卡尔平行成像方法的改进方法2)开发和评估鲁棒非笛卡尔平行成像和压缩感知相结合的方法3)开发和评估基于图形处理单元（gpu）的改进计算方法，用于计算非笛卡尔平行成像。4)验证平行CG-HYPR方法评价心血管疾病作为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.