Print Email Facebook Twitter Stochastic Neighbor Embedding for interactive visualization of flow patterns in 4D flow MRI Title Stochastic Neighbor Embedding for interactive visualization of flow patterns in 4D flow MRI Author de Boer, Mitchell (TU Delft Mechanical Engineering) Contributor Lelieveldt, B.P.F. (mentor) Vos, F.M. (graduation committee) van der Geest, Rob J. (graduation committee) Degree granting institution Delft University of Technology Programme Biomedical Engineering | Medical Physics Date 2024-04-15 Abstract 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. Subject Manifold LearingFlow Visualizationt-SNEHSNE4D Flow AnalysisFlow structuresVortex To reference this document use: http://resolver.tudelft.nl/uuid:ad6afd36-a6dc-40e0-a637-abdebb1ed43f Bibliographical note https://www.dropbox.com/scl/fi/cvfuchp4v4z8npw1fmotm/velocity_timelapse_S112-D2.mp4?rlkey=u98ucfl8k72nbnpvl2z0xjand&dl=0 Video described in figure 4.3 Part of collection Student theses Document type master thesis Rights © 2024 Mitchell de Boer Files PDF Thesis_mitchelldeboer_5184193.pdf 5.76 MB MP4 speed_timelapse_S112-D2_1_.mp4 11.43 MB MP4 Video_fast.mp4 2.61 MB Close viewer /islandora/object/uuid:ad6afd36-a6dc-40e0-a637-abdebb1ed43f/datastream/OBJ2/view