Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
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发表时间：
2017
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通讯作者：
C. Caiafa;O. Sporns;A. Saykin;F. Pestilli
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作者：
C. Caiafa;O. Sporns;A. Saykin;F. Pestilli
最近，已经提出了线性公式和凸优化方法来预测扩散加权磁共振成像(DMRI)数据，给出了使用跟踪算法生成的大脑连接的估计。包括这种方法的线性模型的大小随着dMRI数据和连接体分辨率的增加而增长，并且当应用于现代数据时可以变得非常大。在本文中，我们介绍了一种利用稀疏张量分解来编码dMRI信号和大连接的方法，即那些范围从数十万到数百万束(神经元轴突的束)的连接。我们证明这种张量分解精确地逼近了最近发展起来的线性模型之一的线性分束评估(LIFE)模型。我们对稀疏分解模型LiFESD的精度进行了理论分析，并证明了它可以显著降低模型的规模。此外，我们还开发了算法以高效地使用张量表示来实现优化求解器。
Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFESD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimisation solver using the tensor representation in an efficient way.