MR Image Reconstruction Using a Combination of Compressed Sensing and Partial Fourier Acquisition: ESPReSSo

A Cartesian subsampling scheme is proposed incorporating the idea of PF acquisition and variable-density Poisson Disc (vdPD) subsampling by redistributing the sampling space onto a smaller region aiming to increase k-space sampling density for a given acceleration factor. Especially the normally sparse sampled high-frequency components benefit from this sampling redistribution, leading to improved edge delineation. The prospective subsampled and compacted k-space can be reconstructed by a seamless combination of a CS-algorithm with a Hermitian symmetry constraint accounting for the missing part of the k-space. This subsampling and reconstruction scheme is called Compressed Sensing Partial Subsampling (ESPReSSo) and was tested on in-vivo abdominal MRI datasets. Different reconstruction methods and regularizations are investigated and analyzed via global (intensity-based) and local (region-of-interest and line evaluation) image metrics, to conclude a clinical feasible setup. Results substantiate that ESPReSSo can provide improved edge delineation and regional homogeneity for multidimensional and multi-coil MRI datasets and is therefore useful in applications depending on well-defined tissue boundaries, such as image registration and segmentation or detection of small lesions in clinical diagnostics.

提出了一种笛卡尔欠采样方案，该方案结合了PF采集和可变密度泊松圆盘（vdPD）欠采样的思想，通过将采样空间重新分布到一个更小的区域，旨在针对给定的加速因子提高k空间采样密度。特别是通常稀疏采样的高频成分从这种采样重新分布中受益，从而改善了边缘描绘。预期的欠采样和压缩的k空间可以通过将压缩感知算法与考虑k空间缺失部分的厄米对称性约束无缝结合来重建。这种欠采样和重建方案被称为压缩感知部分欠采样（ESPReSSo），并在体内腹部MRI数据集上进行了测试。通过全局（基于强度）和局部（感兴趣区域和线条评估）图像指标对不同的重建方法和正则化进行了研究和分析，以得出一种临床可行的设置。结果证实，ESPReSSo可以为多维和多线圈MRI数据集提供更好的边缘描绘和区域均匀性，因此在依赖明确组织边界的应用中很有用，例如临床诊断中的图像配准、分割或小病灶检测。