Brain Imaging and Analysis Center, School of Medicine, Duke University, Durham, North Carolina, USA.
Magn Reson Med. 2011 Jan;65(1):83-95. doi: 10.1002/mrm.22545.
Two improved compressed sensing (CS)-based image reconstruction methods for MRI are proposed: prior estimate-based compressed sensing (PECS) and sensitivity encoding-based compressed sensing (SENSECS). PECS allows prior knowledge of the underlying image to be intrinsically incorporated in the image recovery process, extending the use of data sorting as first proposed by Adluru and DiBella (Int J Biomed Imaging 2008: 341648). It does so by rearranging the elements in the underlying image based on the magnitude information gathered from a prior image estimate, so that the underlying image can be recovered in a new form that exhibits a higher level of sparsity. SENSECS is an application of PECS in parallel imaging. In SENSECS, image reconstruction is carried out in two stages: SENSE and PECS, with the SENSE reconstruction being used as a image prior estimate in the following PECS reconstruction. SENSECS bypasses the conflict of sampling pattern design in directly applying CS recovery in multicoil data sets and exploits the complementary characteristics of SENSE-type and CS-type reconstructions, hence achieving better image reconstructions than using SENSE or CS alone. The characteristics of PECS and SENSECS are investigated using experimental data.
提出了两种改进的基于压缩感知（CS）的 MRI 图像重建方法：基于先验估计的压缩感知（PECS）和基于灵敏度编码的压缩感知（SENSECS）。PECS 允许将底层图像的先验知识内在地纳入图像恢复过程中，扩展了 Adluru 和 DiBella（Int J Biomed Imaging 2008：341648）最初提出的数据排序的使用。它通过根据从先前图像估计中收集的幅度信息对底层图像中的元素进行重新排列，从而以表现出更高稀疏度的新形式恢复底层图像。SENSECS 是 PECS 在并行成像中的应用。在 SENSECS 中，图像重建分两个阶段进行：SENSE 和 PECS，其中 SENSE 重建用作后续 PECS 重建的图像先验估计。SENSECS 避免了在多线圈数据集直接应用 CS 恢复时采样模式设计的冲突，并利用了 SENSE 型和 CS 型重建的互补特性，从而实现了比单独使用 SENSE 或 CS 更好的图像重建。使用实验数据研究了 PECS 和 SENSECS 的特性。
