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The aim of this master’s thesis is the design and implementation of a dedicated software system, for planning and implementation of occupational therapy intervention and research studies, in a driving simulator environment. In the first part, the concept based on user requirements is presented. It consists of architectural patterns and guidelines with the main focus on utility and application security. The result of this part is the design of a web application which supports integration in a clinical as well as a research environment. The second part presents the reference implementation of the previously introduced concept. It was developed under a case study in a research facility which hosts a driving simulator. A close cooperation and the influence the researcher’s experience led into a product which provides advanced usability for the target users. In conclusion, the thesis validated the concept indirectly under a testing phase of the reference implementation. It provides the base for a follow-up project to refine the software product and extend the concept to different fields of application.
Ambulant studies are dependent on the behavior and compliance of subjects in their home environment. Especially during interventions on the musculoskeletal system, monitoring physical activity is essential, even for research on nutritional, metabolic, or neuromuscular issues. To support an ambulant study at the German Aerospace Center (DLR), a pattern recognition system for human activity was developed. Everyday activi-ties of static (standing, sitting, lying) and dynamic nature (walking, ascending stairs, descending stairs, jogging) were under consideration. Two tri-axial accelerometers were attached to the hip and parallel to the tibia. Pattern characterizing features from the time domain (mean, standard deviation, absolute maximum) and the frequency domain (main frequencies, spectral entropy, autoregressive coefficients, signal magni-tude area) were extracted. Artificial neural networks (ANN) with a feedforward topology were trained with backpropagation as supervised learning algorithm. An evaluation of the resulting classifier was conducted with 14 subjects completing an activity protocol and a free chosen course of activities. An individual ANN was trained for each subject. Accuracies of 87,99 % and 71,23 % were approached in classifying the activity protocol and the free run, respectively. Reliabilities of 96,49 % and 76,77 % were measured. These performance parameters represent a working ambulant physical activity monitor-ing system.
Aside from hardware, a major component of a Brain Computer Interface is the software that provides the tools for translating raw acquired brain signals into commands to control an application or a device. There’s a range of software, some proprietary, like MATLAB and some free and open source (FOSS), accessible under the GNU General Public License (GNU GPL). OpenViBE is one such freely accessible software. This thesis carries out a functionality and usability test of the platform, looking at its portability, architecture and communication protocols. To investigate the feasibility of reproducing the P300 xDAWN speller BCI presented by OpenViBE, users focused on a character on a 6x6 alphanumeric grid which contained a sequence of random flashes of the rows and columns. Visual stimulus is presented to a user every time the character they are focusing on is highlighted in a row or column. A TMSi analog-to-digital converter was used together with a 32-channel active electrode cap (actiCAP) to record user’s Electroencephalogram (EEG) which was then used in an offline session to train the spatial filter algorithm, and the classifier to identify the P300 evoked potentials, elicited as a user’s reaction to an external stimulus. In an online session, the users tried to spell with the application using the power of their brain signal. Aspects of evoked potentials (EP), both auditory (AEP) and visual (VEP) are further investigated as a validation of results of the P300 speller.
Clinical diagnosis ideally relies on quantitative measures of disease. For a number of diseases, diagnostic guidelines require or at least recommend neuroimaging exams to support the clinical findings. As such, there is also an increasing interest to derive quantitative results from magnetic resonance imaging (MRI) examinations, i.e. images providing quantitative T1, T2, T2* tissue parameters. Quantitative MRI protocols, however, often require prohibitive long acquisition times (> 10 minutes), nor standards have been established to regulate and control MRI-based quantification. This work aims at exploring the technical feasibility to accelerate existing MRI acquisition schemes to enable a -3 minutes clinical imaging protocol of quantitative tissue parameters such as T2 and T2* and at identifying technical factors that are key elements to obtain accurate results. In the first part of this thesis, the signal model of an existing quantitative T2-mapping algorithm is expanded to explore the methodology for a broader use including the application to T2* and its use in the presence of imperfect imaging conditions and system related limitations of the acquisition process. The second part of this thesis is dedicated to optimize the iterative mapping algorithm for a robust clinical application including the integration on a clinical MR platform. This translation of technology is a major step to enable and validate such new methodology in a realistic clinical environment. The robustness and accuracy of the developed and implemented model is investigated by comparing with the "gold standard" information from fully sampled phantom and in-vivo MRI data.
Quantitative assessment of Positron Emission Tomography (PET) imaging can be used for diagnosis and staging of tumors and monitoring of response in cancer treatment. In clinical practice, PET analysis is based on normalized indices such as those based on the Standardized Uptake Value (SUV). Although largely evaluated, these indices are considered quite unstable mainly because of the simplicity of their experimental protocol. Development and validation of more sophisticated methods for the purposes of clinical research require a common open platform that can be used both for prototyping and sharing of the analysis methods, and for their evaluation by clinical users. This work was motivated by the lack of such platform for longitudinal quantitative PET analysis. By following a prototype driven software development approach, an open source tool for quantitative analysis of tumor changes based on multi-study PET image data has been implemented. As a platform for this work, 3D Slicer 4, a free open source software application for medical image computing has been chosen. For the analysis and quantification of PET data, the implemented software tool guides the user through a series of workflow steps. In addition to the implementation of a guided workflow, the software was made extensible by integration of interfaces for the enhancement of segmentation and PET quantification algorithms. By offering extensibility, the PET analysis software tool was transformed into a platform suitable for prototyping and development of PET-specific segmentation and quantification methods. The accuracy, efficiency and usability of the platform were evaluated in reproducibility and usability studies. The results achieved in these studies demonstrate that the implemented longitudinal PET analysis software tool fulfills all requirements for the basic quantification of tumors in PET imaging and at the same time provides an efficient and easy to use workflow. Furthermore, it can function as a platform for prototyping of PET-specific segmentation and quantification methods, which in the future can be incorporated in the workflow.
Sudden cardiac arrest is a leading cause of death world wide, with about 100.000 to 150.000 cases each year in Germany alone [Weidringer and Sefrin, 2006]. This means that annually one out of 1000 citizens are affected [Bahr, 2007]. At standard conditions the human brain has a relative low ischemic1 tolerance. Therefore after 3 - 5 minutes without therapy, irreversible damage is to be expected. The rate of survival drops 7% - 10% each minute, without resuscitation [Bahr, 2007]. Since the arrival of the organized emergency medical service usually takes more than 5 minutes after the emergency call [Wahlen et al., 2003, Weisfeldt et al., 2010], the instant and adequate resuscitation by bystanders in this period is of vital importance. The advantage of basic life support2 (BLS) by laymen shows a fourfold higher rate of survival, once resuscitation has begun, until the arrival of the emergency medical service [Bahr, 2007].
Cytoscape is an open source platform for complex network analysis and visualisation. The Pathway Interaction Database (PID) is a highly structured, curated collection of information about known biomolecular interactions and key cellular processes assembled into signalling pathways. Despite the obvious potential and advantageous usage of both tool (Cytoscape) and information source (PID), there has been no conclusive effort to merge and synergise them. This project aims to make use of the open source characteristics of Cytoscape and optimally visualise the biomolecular interactions found in the PID. This is made possible by the development of a plugin which imports a user-selected pathway file, converts it into a Cytoscape-readable file, and then visualises it. Finally, the user has options to further optimise the pathway by the use of a filter (Barcode – Affymetrix) that removes nodes from the network which are lowly expressed in the Affymetrix microarray data. The user then obtains visual results in a matter of seconds. Additionally, the process of subgraphing nodes through the shortest path method could be applied to the network. This can further assist the user in identifying the molecular pathways of the nodes of interest, a useful feature in network analysis.
Segmentation of the Cerebrospinal Fluid from MRI Images for the Treatment of Disc Herniations
(2010)
About 80 percent of people are affected at some point in their lives by lower back pain, which is one of the most common neurological diseases and reasons for long-term disability in the United States. The symptoms are primarily caused by overly heavy lifting and/or overstretching of the back, leading to a rupture and an outward bulge of an intervertebral disc, which puts pressure on and pinches the nerve fibers of the spine. The most common form is a lumbar disc herniation between the fourth and fifth lumbar vertebra and between the fifth lumbar vertebra and the sacrum. In recent years the diagnosis of lower back pain has improved, mainly due to enhanced imaging techniques and imaging quality, but the surgical therapy remains hazardous. Reasons for this include low visibility when accessing the lumbar area and the high risk of causing permanent damage when touching the nerve fibers. A new approach for increasing patient safety is the segmentation and visualization of the cerebrospinal fluid in the lower lumbar region of the vertebral column. For this purpose a new fully-automatic and a semi-automatic approach were developed for separating the cerebrospinal fluid from its surroundings on T2-weighted MRI scans of the lumbar vertebra. While the fully-automatic algorithm is realized by a model-based searching method and a volume-based segmentation, the semi-automatic algorithm requires a seed point and performs the segmentation on individual axial planes through a combination of a region-based segmentation algorithm and a thresholding filter. Both algorithms have been applied to four T2-weighted MRI datasets and are compared with a gold-standard segmentation. The segmentation overlap with the gold-standard was 78.7 percent for the fully-automatic algorithm and 93.1 percent for the semi-automatic algorithm. In the pathological region the fully-automatic algorithm obtained a similarity of 56.6 percent, compared to 87.8 percent for the semi-automatic algorithm.
This thesis presents a photmetric stereo method based on the work of Schulze [35], who in turn extended the research of Schroeder et al. [33,34] In this approach, three different lightings are obtained by illuminating the object by three colored light sources (red, green and blue). A video of the subject is captured from the front, the back and the side. The single frames are then extracted from the viedo, which are used for the 3D reconstruction of the subject. The aim of this work was to improve the presented method of Schulze with real patient subjects by getting a better sphere calibration and changing some parameters in the patient processing. As the graphical interface was implemented for persons with a technical background, it has been changed to become also more convenient to use for non-technically oriented staff
Access, Handling and Visualization Tools for Multiple Data Types for Breast Cancer Decision Support
(2011)
Breast cancer is the most commonly diagnosed cancer among U.S women, besides skin cancer. More than 1 in 4 cancers among women are breast cancer. And though death rates have been decreasing since 1990, about 40,170 women in the U.S. were expected to die in 2009 from breast cancer. The progress of molecular profiling, in the last decade has revolutionized the understanding of cancer, but also introduced more complexity with new data such as gene expression, copy number variation, mutations and DNA methylation. These new data open up the possibility of differential diagnosis, much more precise prognosis as well as prediction of therapy response than any of the diagnostic tools that are available in the current practice. Additionally, epidemiological databases store clinically relevant information on hundreds of thousands of patients. However, with the abundance of all this information, clinicians will need new tools to access and visualize such data and use the information gained to treat new patients. The general problem will be to access, filter and analyze the data and then visualize them in a clinical context. This data ranges from clinico-pathological information, to molecular profiles from highthroughput genomic measurements and imaging data. Furthermore, data from patient populations is aggregated on epidemiological level and can be found under numerous clinical studies.