Flow visualization is an important topic in many scientific domains and has been an active field of research for many years. Many different methods of analysis can be used in order to analyze flow, however recently big progress have been reported on the manifold learning algorithms for high-dimensional data. This thesis investigates the use of Stochastic Neighbor Embedding (SNE) methods, t-distributed stochastic Neighbour embedding(t-SNE) and hierarchical stochastic Neighbor embedding (HSNE) for flow analysis. In this thesis the Manivault ,Veith et al., 2024, software platform has been used in order to create an interactive analysis tool for SNE methods used on flow data. This tool consists of a 3D viewer plugin that visualizes the full path lines and an existing scatterplot plugin that is used in order to interact with the created SNE maps. The experiments and comparisons reported in this thesis aimed to compare the use of t-SNE and HSNE for analysis of flow structures in 4D Flow MRI data. From this, it can be concluded both t-SNE and HSNE are useful for interaction with and analysis of 4D Flow MRI data. t-SNE can best be used in order to explore and analyze flow data in search for flow structures, and comparing flow patterns between subjects. HSNE on the other hand gives a better separation between different flow components, however at the expense of a less accurate preservation of vortices and other flow structures.

Hypertrophic cardiomyopathy (HCM) is known as a frequent, genetic cardiovascular disease, often caused by mutations of sarcomere protein genes. HCM is primarily characterized by the presence of an increased left ventricular wall thickness, i.e. left ventricular hypertrophy (LVH). However, the disease appears to be asymptomatic in some patients, which makes it a diagnostic challenge. Mutation carriers of HCM who have not yet developed LVH are called genotype-positive left ventricular hypertrophy-negative (G+/LVH-) patients. The primary aim of this study was to investigate whether a radiomics model is able to distinguish between G+/LVH- patients and healthy controls, based on cardiac magnetic resonance (CMR) images. In total three datasets are analysed. A development dataset was used to develop different radiomics models and to evaluate the performance of the models. The models were validated on both the prospective validation dataset and external validation dataset. G+/LVH- patients had to be known to carry a class 4 (likely pathogenic) or class 5 (pathogenic) gene mutation for HCM and a maximum left ventricular wall thickness of <13mm. Endocardial and epicardial borders were manually and automatically segmented on long-axis view (2-chamber (2CH), 3-chamber (3CH), and 4-chamber (4CH)) and short-axis (SA) view in both end-diastolic (ED) and end-systolic (ES) phase. From these segmentation 555 features including shape, intensity and texture were extracted. Evaluation of radiomics models was performed through a 100x stratified random-split cross-validation in development dataset. Next, the models were validated on prospective validation dataset and external validation dataset. The radiomics model with best performance developed on development dataset had a mean area under the receiver operating characteristic curve (AUC) of 0.89. A similar performance in prospective validation was found (mean AUC of 0.89), while a lower performance was found in external validation dataset (mean AUC of 0.63). In addition, the radiomics models performed with automatic segmentation showed all a decrease in performance; mean AUC of 0.75, 0.77 and 0.50 in development dataset, prospective validation dataset and external validation dataset, respectively. Our radiomics models using CMR images can non-invasively distinguish between +/LVH- patients and healthy controls on both development dataset and prospective validation dataset. However, it was not able to distinguish on external validation dataset.