Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI
Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI
复制标题
DOI：
10.1016/j.media.2019.02.014
复制
发表时间：
2019-05-01
影响因子：
10.9
通讯作者：
Vemuri，C.
中科院分区：
文献类型：
--
作者：
Sun，Jiaqi;Entezari，Alireza;Vemuri，C.
根据欠采样扩散MRI（dMRI）测量结果准确重建系综平均传播子（EAPs）是dMRI采集和分析领域中一个积极研究的问题。已经针对该问题开发了许多基于压缩感测（CS）原理的方法，通过利用信号的稀疏表示来实现采集中的相当大的加速。文献中的最新方法在（k，q）空间中应用欠采样技术以在联合（x，r）空间中恢复EAP。然而，这些方法中的大多数遵循首先在（x，q）空间中重建扩散图像并且随后通过3D傅立叶变换估计EAP的流水线。在这项工作中，我们提出了一种新的方法来实现直接重建ofP（x，r）从部分（k，q）-空间测量，几何约束涉及平行的水平集的扩散图像从邻近q-空间点。通过直接从（k，q）空间数据重建P（x，r）），我们充分利用了6D感知域和重建域之间的不相干性，这与CS理论是一致的。此外，我们的方法的目的是利用固有的结构相似性（并行性）的扩散图像中的水平集对应于邻近locatedq空间点在CS框架中，以实现进一步降低样本的复杂性，可以促进更快的收购dMRI。我们比较了所提出的方法，以一个国家的最先进的CS为基础的EAP重建方法（从联合（k，q）空间）模拟，幻影和真实的dMRI数据证明利用q空间中的结构相似性的好处。
Accurate reconstruction of the ensemble average propagators (EAPs) from undersampled diffusion MRI (dMRI) measurements is a well-motivated, actively researched problem in the field of dMRI acquisition and analysis. A number of approaches based on compressed sensing (CS) principles have been developed for this problem, achieving a considerable acceleration in the acquisition by leveraging sparse representations of the signal. Most recent methods in literature apply undersampling techniques in the (k, q)-space for the recovery of EAP in the joint (x, r)-space. Yet, the majority of these methods follow a pipeline of first reconstructing the diffusion images in the (x, q)-space and subsequently estimating the EAPs through a 3D Fourier transform. In this work, we present a novel approach to achieve the direct reconstruction ofP(x, r) from partial (k, q)-space measurements, with geometric constraints involving the parallelism of level-sets of diffusion images from proximalq-space points. By directly reconstructingP(x, r)) from (k, q)-space data, we exploit the incoherence between the 6D sensing and reconstruction domains to the fullest, which is consistent with the CS-theory. Further, our approach aims to utilize the inherent structural similarity (parallelism) of the level-sets in the diffusion images corresponding to proximally-locatedq-space points in a CS framework to achieve further reduction in sample complexity that could facilitate faster acquisition in dMRI. We compare the proposed method to a state-of-the-art CS based EAP reconstruction method (from joint (k, q)-space) on simulated, phantom and real dMRI data demonstrating the benefits of exploiting the structural similarity in theq-space.