Microstructural parameter estimation in vivo using diffusion MRI and structured prior information.

Diffusion MRI has recently been used with detailed models to probe tissue microstructure. Much of this work has been performed ex vivo with powerful scanner hardware, to gain sensitivity to parameters such as axon radius. By contrast, performing microstructure imaging on clinical scanners is extremely challenging

Massiv-Parallele Algorithmen zum Laden von Daten auf Moderner Hardware

While systems face an ever-growing amount of data that needs to be ingested, queried and analysed, processors are seeing only moderate improvements in sequential processing performance. This thesis addresses the fundamental shift towards increasingly parallel processors and contributes multiple massively parallel algorithms to accelerate different stages of the ingestion pipeline, such as data parsing and sorting.Systeme sehen sich mit einer stetig anwachsenden Menge an Daten konfrontiert, die geladen und analysiert, sowie Anfragen darauf bearbeitet werden müssen. Gleichzeitig nimmt die sequentielle Verarbeitungsgeschwindigkeit von Prozessoren nur noch moderat zu. Diese Arbeit adressiert den Wandel hin zu zunehmend parallelen Prozessoren und leistet mit mehreren massiv-parallelen Algorithmen einen Beitrag um unterschiedliche Phasen der Datenverarbeitung wie zum Beispiel Parsing und Sortierung zu beschleunigen

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.Comment: Preprint to be published in NeuroImag