论文标题
技术报告（v1.0） - 动态MRI的伪随机笛卡尔采样
Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI
论文作者
论文摘要
为了有效地应用压缩传感（CS），从而利用了图像的潜在可压缩性，因此要求之一是，在稀疏的变换域中，不连贯的伪像（噪声样）是不一致的（噪声样）。对于心血管MRI（CMR），已经提出了几种伪随机抽样方法，它们产生了高水平的不连贯性。在这份技术报告中，我们提供了五种伪造的笛卡尔抽样方法的集合，这些方法可应用于2D Cine and Flow，3D体积Cine和4D流量成像。在五个提出的方法中，有四种可以快速计算采样蒙版，而无需创建和存储预先计算的查找表。另外，对采样分布进行了参数化，从而控制了采样密度。对于报告中的每种采样方法，（i）我们简要描述了相关参数的默认值，并且（iii）提供了公开可用的MATLAB实现。
For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain. For cardiovascular MRI (CMR), several pseudo-random sampling methods have been proposed that yield a high level of incoherence. In this technical report, we present a collection of five pseudo-random Cartesian sampling methods that can be applied to 2D cine and flow, 3D volumetric cine, and 4D flow imaging. Four out of the five presented methods yield fast computation for on-the-fly generation of the sampling mask, without the need to create and store pre-computed look-up tables. In addition, the sampling distribution is parameterized, providing control over the sampling density. For each sampling method in the report, (i) we briefly describe the methodology, (ii) list default values of the pertinent parameters, and (iii) provide a publicly available MATLAB implementation.
