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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.