IEEE Trans Med Imaging. 2022 Feb;41(2):446-455. doi: 10.1109/TMI.2021.3115716. Epub 2022 Feb 2.
Many biological tissues contain an underlying fibrous microstructure that is optimized to suit a physiological function. The fiber architecture dictates physical characteristics such as stiffness, diffusivity, and electrical conduction. Abnormal deviations of fiber architecture are often associated with disease. Thus, it is useful to characterize fiber network organization from image data in order to better understand pathological mechanisms. We devised a method to quantify distributions of fiber orientations based on the Fourier transform and the Qball algorithm from diffusion MRI. The Fourier transform was used to decompose images into directional components, while the Qball algorithm efficiently converted the directional data from the frequency domain to the orientation domain. The representation in the orientation domain does not require any particular functional representation, and thus the method is nonparametric. The algorithm was verified to demonstrate its reliability and used on datasets from microscopy to show its applicability. This method increases the ability to extract information of microstructural fiber organization from experimental data that will enhance our understanding of structure-function relationships and enable accurate representation of material anisotropy in biological tissues.
许多生物组织都含有优化以适应生理功能的基础纤维微观结构。纤维结构决定了诸如刚度、扩散率和电导率等物理特性。纤维结构的异常偏差通常与疾病有关。因此，从图像数据中对纤维网络组织进行特征描述以更好地了解病理机制是很有用的。我们设计了一种基于傅里叶变换和扩散 MRI 中的 Qball 算法来量化纤维方向分布的方法。傅里叶变换用于将图像分解为方向分量，而 Qball 算法则有效地将方向数据从频域转换到方向域。在方向域中的表示不需要任何特定的函数表示，因此该方法是非参数的。该算法经过验证以证明其可靠性，并在显微镜数据集上使用以展示其适用性。这种方法提高了从实验数据中提取微观结构纤维组织信息的能力，这将增强我们对结构-功能关系的理解，并能够准确表示生物组织中的材料各向异性。
