论文标题
Core-Deblur：使用压缩感应进行脱毛的平行MRI重建
CORE-Deblur: Parallel MRI Reconstruction by Deblurring Using Compressed Sensing
论文作者
论文摘要
在这项工作中，我们引入了一种新方法，该方法将并行MRI和压缩传感（CS）结合起来，以通过亚采样K-Space数据加速图像重建。该方法首先计算一个卷曲的图像，该图像给出了用户定义的内核和未知MR图像之间的卷积，然后通过基于CS的基于CS的图像Deblurring重建图像，其中CS应用于从卷积过程中删除固有的模糊词。因此，该方法称为核心核能。回顾性的次采样实验，该实验具有来自数值脑幻影和体内7T脑扫描的数据，表明核心偏晶型产生了与常规CS方法相媲美的高质量重建，同时将迭代次数减少10倍或更多。通过Core-Deblur为体内数据集获得的平均归一化均方根误差（NRMSE）为0.016。 Core-Deblur在选定的内核以及与各种K空间亚采样方案的兼容性方面也表现出鲁棒性，从常规到随机。总而言之，核心核能可以实现高质量的重建，并将CS迭代数减少10倍。
In this work we introduce a new method that combines Parallel MRI and Compressed Sensing (CS) for accelerated image reconstruction from subsampled k-space data. The method first computes a convolved image, which gives the convolution between a user-defined kernel and the unknown MR image, and then reconstructs the image by CS-based image deblurring, in which CS is applied for removing the inherent blur stemming from the convolution process. This method is hence termed CORE-Deblur. Retrospective subsampling experiments with data from a numerical brain phantom and in-vivo 7T brain scans showed that CORE-Deblur produced high-quality reconstructions, comparable to those of a conventional CS method, while reducing the number of iterations by a factor of 10 or more. The average Normalized Root Mean Square Error (NRMSE) obtained by CORE-Deblur for the in-vivo datasets was 0.016. CORE-Deblur also exhibited robustness regarding the chosen kernel and compatibility with various k-space subsampling schemes, ranging from regular to random. In summary, CORE-Deblur enables high quality reconstructions and reduction of the CS iterations number by 10-fold.
