| 1. |
- Brun, Anders, 1976-
(författare)
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Manifold learning and representations for image analysis and visualization
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. This coordinate system is in a way a generalization of Cartesian coordinates in Euclidean space. We translate this idea to a cloud of data points in order to perform dimension reduction. Our implementation currently uses Dijkstra's algorithm for shortest paths in graphs and some basic concepts from differential geometry. We expect this approach to open up new possibilities for analysis of e.g. shape in medical imaging and signal processing of manifold-valued signals, where the coordinate system is “learned” from experimental high-dimensional data rather than defined analytically using e.g. models based on Lie-groups.We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps (Belkin and Niyogi, 2002), we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.
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| 2. |
- Eklund, Anders
(författare)
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Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works.This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI).Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface.Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis.Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.
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| 3. |
- Hermansson, Jonas
(författare)
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Shift work and cardiovascular disease
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Shift work is a work schedule being the opposite of normal daytime work, often defined as working time outside normal daytime hours (06:00 to 18:00). In recent years, shift work has been associated with an increased risk of numerous chronic conditions including for example cardiovascular disease, some types of cancer, type II diabetes, and the metabolic syndrome. While some studies on the association between shift work and chronic disease have found results supporting it, others have not. Therefore, more research is needed to clarify potential associations.The aim of this thesis was to further study the proposed association between shift work and cardiovascular disease. This was addressed by performing two studies, one analysing if shift workers had an increased risk of ischemic stroke compared to day workers. The other study analysed whether shift workers had an increased risk of short-term mortality (case fatality) after a myocardial infarction compared to day workers. The studies were performed using logistic regression analysis in two different case-control databasesThe findings from the first study indicated that shift workers did not have an increased risk of ischemic stroke. The findings from the second study showed that male shift workers had an increased risk of death within 28 days after a myocardial infarction; the results did not indicate an increased risk for female shift workers. The results from both studies were adjusted for both behavioural and medical risk factors without affecting the results. The findings from this thesis provide new evidence showing that male shift workers have an increased risk of death 28 days after a myocardial infarction, however more research is needed to clarify and characterise any such potential associations.
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