Since diffusion is particularly disturbed in tissues with high cell densities such as tumors, diffusion-weighted imaging (DWI) constitutes an essential tool for the detection and characterization of lesions in modern MRI-based diagnostics. However, despite the great influence and frequent use of DWI, the image quality obtained is still variable, which can lead to false diagnoses or costly follow-up examinations.
A common way to increase the signal-to-noise ratio (SNR) in MR imaging is to repeat the acquisition several times, i.e. use multiple number of excitations (NEX). The final image is then calculated by ordinary averaging. While the single images are relatively unaffected by bulk motion due to the short acquisition time, relative motion between the excitations and subsequent averaging will lead to motion blurring in the final image. One way to mitigate this is to perform prospective gating (also known as triggering) using a respiratory signal. However, triggered acquisitions come at the cost of significantly increased scan time. Retrospective gating (also known as binning) constitutes an alternative approach in which data is acquired continuously and subsequently assigned to discrete motion states. The drawback of this approach is that there is no guarantee that data is collected for a given slice within the target motion state. In previous works, mapping of the images from other motion states onto the target motion state was achieved by using a motion model given by an additional navigator acquisition.
In recent years, deep learning has shown great potential in the field of MRI in a wide variety of applications. The goal of this thesis is the development of a deep learning-based algorithm which performs navigator-free registration of DW images given a respiratory signal only. Missing data for certain motion states as well as the inherently low SNR of DW images constitute the main challenges of this work. Successful completion of this work promises significant improvements in image quality for diffusion-weighted imaging in motion-sensitive body regions such as the abdomen.