Novel Deep Learning Approaches for Analyzing Diffusion Imaging Data

近年来，弥散成像（diffusion imaging，DI）迅速发展成为临床脑研究的重要非侵入性工具之一。然而，由于所需的梯度方向数量，测量时间较长，导致临床实践中很少使用DI。为了克服这个问题，最近的方法证明了机器学习的力量，特别是深度学习（DL），它能够非常准确地描述和重建组织的底层复杂功能，而只需要很少的梯度方向。因此，扫描时间可以大大减少。然而，对于DL在临床DI中的最佳适用性，确定了四个主要障碍，这些障碍将在本项目中得到解决。最大的障碍是来自不同MRI系统的数据之间的巨大差异。为了克服这一障碍，现有的方法协调不同的MRI系统将进行比较，并协调MRI信号的最佳方法将developed.Next，地面真实数据的需要，这是很难获得的DI，复杂的DL方法的训练。为了解决这个问题，将开发一个框架，读取数据集，以确定重要的扩散特征和统计数据。随后，可以基于该信息来合成各个扩散数据并且由此合成完整的扩散数据集。在训练过程中，可以使用得到的数据及其对应的地面真值来提高DL模型的性能。此外，由于在常规重建方法中很少使用，通常在采集过程中被丢弃的复信号被集成到使用新型DL方法的重建中。研究表明，复杂的MRI信号携带重要的组织信息，因此可以在DL网络内的重建期间用作附加信息。为此，需要开发能够处理复杂信号的新DL组件。在这个项目的最后，重点在于每个体素的角度相关的扩散信号。先前的DL方法当前不能将该附加的球面信息并入到处理中，这就是为什么需要将先前的DL元素转移到球面上并将它们链接到正常DL元素的新方法的原因。在整个项目的前半部分，将在各个位置采集MRI数据，包括其相位数据，大量的梯度方向和高分辨率，以评估所描述的所有方法。该项目的目的和由此产生的方法是显着减少在临床实践中的扩散成像序列的扫描时间，同时保持相同的精度。

Recently, diffusion imaging (DI) rapidly developed into one of the most important non-invasive tools for clinical brain research. However, long measurement times, due to the required number of acquired gradient directions, result in a rare usage of DI in clinical practice. To overcome this problem, recent methods demonstrated the strength of machine learning and in particular deep learning (DL), which is able to describe and reconstruct the tissue's underlying complex functions very accurately while only few gradient directions are required. Thus, scanning time can be greatly reduced.For an optimal applicability of DL in clinical DI, however, four major obstacles were identified which will be addressed within this project. The biggest barrier is the large variance between data from different MRI systems. To overcome this barrier, existing methods for harmonizing different MRI systems will be compared and an optimal method for harmonizing MRI signals will be developed.Next, the need of ground truth data is addressed, which is difficult to obtain in DI, complicating the training of DL methods. To solve this problem, a framework will be developed that reads in a dataset, to determine important diffusion characteristics and statistics. Subsequently, individual diffusion data and thereby a complete diffusion dataset can be synthesized based on this information. The resulting data and its corresponding ground truth can later be used during training to improve the DL model’s performance.Furthermore, complex signals, which are commonly discarded during acquisition, due to their rare usage in regular reconstruction methods, are integrated into the reconstruction utilizing novel DL methods. Studies have shown that complex MRI signals carry important tissue information, which could therefore be used as additional information during reconstruction within DL networks. For this purpose, new DL components that are capable of processing complex signals need to be developed. At the end of this project, the focus lies on the angle-related diffusion signals per voxel. Previous DL methods are currently not able to incorporate this additional spherical information into the processing, which is why new methods are needed that transfer the previous DL elements onto a sphere and link them to normal DL elements. In this way, neighboring information within the signal as well as between signals can be included to ensure optimal reconstruction.Throughout the first half of the project, MRI data, including its phase data, a high number of gradient directions and a high resolution will be acquired at various locations to evaluate all the methods described. The aim of this project and the resulting methods is to significantly reduce the scan times for diffusion imaging sequences in clinical practice while maintaining the same accuracy.