A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging.
A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging.
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DOI：
10.1016/j.neuroimage.2015.10.061
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发表时间：
2016-01-15
期刊：
影响因子：
5.7
通讯作者：
Rathi Y
中科院分区：
文献类型：
--
作者：
Ning L;Setsompop K;Michailovich O;Makris N;Shenton ME;Westin CF;Rathi Y
弥散MRI（dMRI）可以提供有关大脑中不同组织类型结构的宝贵信息。标准dMRI采集有助于对中等至较大的白色物质束进行适当分析（例如，追踪）。然而，连接非常小的皮质或皮质下区域的较小的纤维束不能在具有大体素尺寸的图像中准确地追踪。然而，追踪这种纤维束的能力对于诸如脑深部电刺激和神经外科手术等几种应用至关重要。在这项工作中，我们提出了一种新的采集和重建方案，用于获得高空间分辨率的dMRI图像，使用多个低分辨率（LR）的图像，这是有效的，在减少采集时间，同时提高信噪比（SNR）。所提出的方法称为压缩感知超分辨率重建（CS-SRR），使用多个重叠的厚切片dMRI体积，在q空间中欠采样，以重建具有复杂方向的扩散信号。所提出的方法结合了压缩感知和超分辨率的孪生概念，在具有全变差（TV）正则化的球形脊波的基础上对扩散信号（在给定的b值下）进行建模，以考虑相邻体素中的信号相关性。提出了一种基于交替方向乘子法（ADMM）的高效算法来求解CS-SRR问题。所提出的方法的性能进行了定量评估的几个体内人体数据集，包括一个真正的SRR的情况。我们的实验结果表明，该方法可以用于重建亚毫米超分辨率dMRI数据具有非常好的数据保真度在临床可行的采集时间。
Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.