Diffusion magnetic resonance imaging (dMRI) of biological systems most often results in non-monoexponential signal, due to their complexity and heterogeneity. One approach to interpreting dMRI data without imposing tissue microstructural models is to fit the signal to a multiexponential function, which is sometimes referred to as an inverse Laplace transformation, and to display the coefficients as a distribution of the diffusivities, or water mobility spectra. Until recently, this method has not been used in a voxelwise manner, mainly because of heavy data requirements. With recent advancements in processing and experimental design, voxelwise Laplace MRI approaches are becoming feasible and attractive. The rich spectral information, combined with a three-dimensional image, presents a challenge because it tremendously increases the dimensionality of the data and requires a robust method for interpretation and analysis. In this work, we suggest parameterizing the empirically measured water mobility spectra using a bimodal lognormal function. This approach allows for a compact representation of the spectrum, and it also resolves overlapping spectral peaks, which allows for a robust extraction of their signal fraction. We apply the method on a fixed spinal cord sample and use it to generate robust intensity images of slow- and fast-diffusion components. Using the parametric variables, we create novel image contrasts, among them the information entropy of the water mobility spectrum, which pack unique features of the individual diffusion regimes in the investigated system.

中文翻译：

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脊髓的水迁移谱成像：无模型Laplace MRI的参数化。

生物系统的扩散磁共振成像（dMRI）由于其复杂性和异质性，通常会产生非单指数信号。在不施加组织微结构模型的情况下解释dMRI数据的一种方法是使信号适合多指数函数（有时称为拉普拉斯逆变换），并将系数显示为扩散率或水迁移率光谱的分布。直到最近，主要由于大量数据需求，尚未以立体像素方式使用此方法。随着处理和实验设计的最新进展，三维像素拉普拉斯MRI方法正变得可行和有吸引力。丰富的光谱信息，再加上三维图像，提出挑战是因为它极大地增加了数据的维数，并且需要一种可靠的方法来进行解释和分析。在这项工作中，我们建议使用双峰对数正态函数对经验测得的水迁移率谱进行参数化。这种方法可以紧凑地表示频谱，还可以解决重叠的频谱峰，从而可以可靠地提取其信号分数。我们将这种方法应用于固定的脊髓样本，并使用它来生成慢速和快速扩散成分的强度图像。使用参数变量，我们创建了新颖的图像对比度，其中包括水迁移谱的信息熵，其中包含了所研究系统中各个扩散机制的独特特征。