Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery
Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery
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DOI：
10.1109/camsap.2017.8313115
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
K. Slavakis;Gaurav N. Shetty;Abhishek Bose;Ukash Nakarmi;L. Ying
中科院分区：
文献类型：
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作者：
K. Slavakis;Gaurav N. Shetty;Abhishek Bose;Ukash Nakarmi;L. Ying
建立了欧氏空间中位于(未知)光滑流形上或附近的数据的建模框架，并考虑了其在动态磁共振成像(DMRI)中的应用。该框架包括几个模块：首先，识别一组地标点，以简洁地描述由高度欠采样的dMRI数据形成的数据云；其次，计算地标点的低维再现。搜索将低维数据解压缩为高维数据的线性算子，以及通过仿射面片逼近流形数据的地标点的组合，导致dMRI数据的双线性模型，已知固有的数据几何。对合成的dMRI模体进行了初步的数值测试，并与最先进的重建技术进行了比较，强调了所提出的方法在恢复高度欠采样的dMRI数据方面的巨大潜力。
This paper establishes a modeling framework for data located onto or close to (unknown) smooth manifolds, embedded in Euclidean spaces, and considers its application to dynamic magnetic resonance imaging (dMRI). The framework comprises several modules: First, a set of landmark points is identified to describe concisely a data cloud formed by highly under-sampled dMRI data, and second, low-dimensional renditions of the landmark points are computed. Searching for the linear operator that decompresses low-dimensional data to high-dimensional ones, and for those combinations of landmark points which approximate the manifold data by affine patches, leads to a bi-linear model of the dMRI data, cognizant of the intrinsic data geometry. Preliminary numerical tests on synthetically generated dMRI phantoms, and comparisons with state-of-the-art reconstruction techniques, underline the rich potential of the proposed method for the recovery of highly under-sampled dMRI data.