Analysis of complex data is still a challenge in medical image analysis. Due to the heterogeneous information that can be extracted from magnetic resonance imaging (MRI) it can be difficult to fuse such data in a proper way. One interesting case is given by the analysis of diffusion imaging (DI) data. DI techniques give an important variety of information about the status of microstructure in the brain. This is interesting information to use especially in longitudinal setting where the temporal evolution of the pathology is an important added value. In this paper, we propose a new tensor-based framework capable to detect longitudinal changes appearing in DI data in multiple sclerosis (MS) patients. We focus our attention to the analysis of longitudinal changes occurring along different white matter (WM) fiber-bundles. Our main goal is to detect which subset of fibers (within a bundle) and which sections of these fibers contain "pathological" longitudinal changes. The framework consists of three main parts: i) preprocessing of longitudinal diffusion acquisitions and WM fiber-bundles extraction, ii) data tensorization and rank selection, iii) application of a parallelized constrained tensor factorization algorithm to detect longitudinal "pathological" changes. The proposed method was applied on simulated longitudinal variations and on real MS data. High level of accuracy and precision were obtained in the detection of small longitudinal changes along the WM fiber-bundles.

中文翻译：

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约束张量分解，用于扩散成像数据的纵向分析。

复杂数据的分析仍然是医学图像分析中的挑战。由于可以从磁共振成像（MRI）中提取异类信息，因此很难以适当的方式融合此类数据。通过扩散成像（DI）数据的分析给出了一种有趣的情况。DI技术可提供有关大脑微结构状态的重要信息。这是有趣的信息，尤其是在纵向环境中使用时，病理的时间演变是重要的附加值。在本文中，我们提出了一种基于张量的新框架，该框架能够检测多发性硬化症（MS）患者DI数据中出现的纵向变化。我们将注意力集中在分析沿不同白质（WM）纤维束发生的纵向变化。我们的主要目标是检测哪些纤维子集（在束内）以及这些纤维的哪些部分包含“病理性”纵向变化。该框架包括三个主要部分：i）纵向扩散采集和WM光纤束提取的预处理，ii）数据张量和等级选择，iii）应用并行约束张量因子分解算法以检测纵向“病理”变化。所提出的方法被应用于模拟的纵向变化和真实的MS数据。在检测沿WM纤维束的小的纵向变化时，获得了很高的准确性和精度。i）纵向扩散采集和WM纤维束提取的预处理，ii）数据张量和等级选择，iii）应用并行约束张量分解算法以检测纵向“病理”变化。所提出的方法被应用于模拟的纵向变化和真实的MS数据。在检测沿WM纤维束的小的纵向变化时，获得了很高的准确性和精度。i）纵向扩散采集和WM纤维束提取的预处理，ii）数据张量和等级选择，iii）应用并行约束张量因子分解算法来检测纵向“病理”变化。该方法被应用于模拟的纵向变化和真实的MS数据。在检测沿WM纤维束的小的纵向变化时，获得了很高的准确性和精度。