Novel Deep Learning Approaches for Analyzing Diffusion Imaging Data

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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.