Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. Parameters useful to the system are suggested. Some of these, such as age and gender, should be provided by the physician, and some should be derived from electro- and phonocardiographic signals. Various methods of signal processing, such as wavelet theory and principal components analysis, are described. Heart failure should be diagnosed based on the parameters, and so various forms of decision support systems, such as neural networks and support vector machines, are described. The methods of signal processing and classification are discussed and suggestions on how to develop the system are made.

A Riemannian manifold is a mathematical concept that generalizes curved surfaces to higher dimensions, giving a precise meaning to concepts like angle, length, area, volume and curvature. A glimpse of the consequences of a non-flat geometry is given on the sphere, where the shortest path between two points – a geodesic – is along a great circle. Different from Euclidean space, the angle sum of geodesic triangles on the sphere is always larger than 180 degrees. Signals and data found in applied research are sometimes naturally described by such curved spaces. This dissertation presents basic research and tools for the analysis, processing and visualization of such manifold-valued data, with a particular emphasis on future applications in medical imaging and visualization. Two-dimensional manifolds, i.e. surfaces, enter naturally into the geometric modelling of anatomical entities, such as the human brain cortex and the colon. In advanced algorithms for processing of images obtained from computed tomography (CT) and ultrasound imaging (US), images themselves and derived local structure tensor fields may be interpreted as two- or three-dimensional manifolds. In diffusion tensor magnetic resonance imaging (DT-MRI), the natural description of diffusion in the human body is a second-order tensor field, which can be related to the metric of a manifold. A final example is the analysis of shape variations of anatomical entities, e.g. the lateral ventricles in the brain, within a population by describing the set of all possible shapes as a manifold. Work presented in this dissertation include: Probabilistic interpretation of intrinsic and extrinsic means in manifolds. A Bayesian approach to filtering of vector data, removing noise from sampled manifolds and signals. Principles for the storage of tensor field data and learning a natural metric for empirical data. The main contribution is a novel class of algorithms called LogMaps, for the numerical estimation of logp (x) from empirical data sampled from a low-dimensional manifold or geometric model embedded in Euclidean space. The logp (x) function has been used extensively in the literature for processing data in manifolds, including applications in medical imaging such as shape analysis. However, previous approaches have been limited to manifolds where closed form expressions of logp (x) have been known. The introduction of the LogMap framework allows for a generalization of the previous methods. The application of LogMaps to texture mapping, tensor field visualization, medial locus estimation and exploratory data analysis is also presented. / The electronic version is corrected for grammatical and spelling errors.

Methods for measuring deformation and motion of the human heart in-vivo are crucial in the assessment of cardiac function. Applications ranging from basic physiological research, through early detection of disease to follow-up studies, all rely on the quality of the measurements of heart dynamics. This thesis presents new improved magnetic resonance imaging methods for acquisition, image reconstruction and visualization of cardiac motion and deformation.As the heart moves and changes shape during the acquisition, synchronization to the heart dynamics is necessary. Here, a method to resolve not only the cardiac cycle but also the respiratory cycle is presented. Combined with volumetric imaging, this produces a five-dimensional data set with two cyclic temporal dimensions. This type of data reveals unique physiological information, such as interventricular coupling in the heart in different phases of the respiratory cycle.The acquisition can also be sensitized to motion, measuring not only the magnitude of the magnetization but also a signal proportional to local velocity or displacement. This allows for quantification of the motion which is especially suitable for functional study of the cardiac deformation. In this work, an evaluation of the influence of several factors on the signal-to-noise ratio is presented for in-vivo displacement encoded imaging. Additionally, an extension of the method to acquire multiple displacement encoded slices in a single breath hold is also presented.Magnetic resonance imaging is usually associated with long scan times, and many methods exist to shorten the acquisition time while maintaining acceptable image quality. One class of such methods involves acquiring only a sparse subset of k-space. A special reconstruction is then necessary in order to obtain an artifact-free image. One family of these reconstruction techniques tailored for dynamic imaging is the k-t BLAST approach, which incorporates data-driven prior knowledge to suppress aliasing artifacts that otherwise occur with the sparse sampling. In this work, an extension of the original k-t BLAST method to two temporal dimensions is presented and applied to data acquired with full coverage of the cardio-respiratory cycles. Using this technique, termed k-t2 BLAST, simultaneous reduction of scan time and improved spatial resolution is demonstrated. Further, the loss of temporal fidelity when using the k-t BLAST approach is investigated, and an improved reconstruction is proposed for the application of cardiac function analysis.Visualization is a crucial part of the imaging chain. Scalar data, such as regular anatomical images, are straightforward to display. Myocardial strain and strain-rate, however, are tensor quantities which do not lend themselves to direct visualization. The problem of visualizing the tensor field is approached in this work by combining a local visualization that displays all degrees of freedom for a single tensor with an overview visualization using a scalar field representation of the complete tensor field. The scalar field is obtained by iterated adaptive filtering of a noise field, creating a continuous geometrical representation of the myocardial strain-rate tensor field.The results of the work presented in this thesis provide opportunities for improved imaging of myocardial function, in all areas of the imaging chain; acquisition, reconstruction and visualization.

The aim of this thesis is to develop and study a method for estimation of the pulse pressure in centrally located arteries. Obtaining the central pulse pressure is desirable for several reasons. For example, the central pulse pressure can be used to assess aortic stiffness, which in turn is an important predictor of cardiovascular mortality. In this thesis a method of estimation based on a one--dimensional wave propagation theory applied to a physiological model of the human systemic arterial tree is studied. For the purpose of validation, recorded pressure signals from twenty four control subjects are used. Various methods for individualization of the tree model are discussed, and a method that utilizes an optimization routine is proposed.

A brain-computer interface, BCI, is a technical system that allows a person to control the external world without relying on muscle activity. This thesis presents an EEG based BCI designed for automatic classification of two dimensional hand movements. The long-term goal of the project is to build an intuitive communication system for operation by people with severe motor impairments. If successful, such system could for example be used by a paralyzed patient to control a word processor or a wheelchair. The developed BCI was tested in an offine pilot study. In response to an external cue, a test subject moved a joystick in one of four directions. During the movement, EEG was recorded from seven electrodes mounted on the subject's scalp. An autoregressive model was fitted to the data, and the extracted coefficients were used as input features to a neural network based classifier. The classifier was trained to recognize the direction of the movements. During the first half of the experiment, real physical movements were performed. In the second half, subjects were instructed just to imagine the hand moving the joystick, but to avoid any muscle activity. The results of the experiment indicate that the EEG signals do in fact contain extractable and classifiable information about the performed movements, during both physical and imagined movements.

Because of their compatibility and precise results bio-optical methods based on measurements of the optical tissue properties gain importance in non-invasive medical therapy and diagnostic. For development and standardization of medical devices optical phantoms are suitable. The present report handles the creation and evaluation of solid tissue phantoms, made up of Agar, Vasolipid and ink utilizing different mixture ratios. After cutting the models in slices of 0.2 to 1.1 mm thickness the absorption- and scattering coefficient were measured using a collimated laser beam setup. As result of the study a formula for the preparation of solid optical tissue phantoms with desired optical properties was found, that is valid for models containing 1.12 % Agar.

Because of the difficulties in concluding whether a person has drowned or not, information that could be relevant for postmortal diagnosis of drowning was studied. With postmortal CT images lung volume, mean attenuation, anterior-posterior difference, lung density profile and amount of water within the lungs were investigated. The report also evaluates three examples of software that calculates lung volume from postmortal CT images: Siemens’ Syngo Pulmo CT, Siemens‘ Volume Evaluation and GE Medical Systems’ Volume Viewer. The method used at autopsy was also studied. The repeatability and validity were tested and sources of errors identified. Repeatability and validity for the three tested types of software were acceptable, while the method used at autopsy had to be improved. The study also showed that lung volume related to length, anterior-posterior difference and lung density profile seemed to vary between drowned and other deceased. These measures might conclude whether a person has drowned.

To properly display digital X-ray images for visual diagnosis, a proper display range needs to be identified. This can be difficult when the image contains collimators or large background areas which can dominate the histograms. Also, when there are both underexposed and overexposed areas in the image it is difficult to display these properly at the same time. The purpose of this thesis is to find a way to solve these problems. A few different approaches are evaluated to find their strengths and weaknesses. Based on Local Histogram Equalization, a new method is developed to put various constraints on the mapping. These include alternative ways to perform the histogram calculations and how to define the local histograms. The new method also includes collimator detection and background suppression to keep irrelevant parts of the image out of the calculations. Results show that the new method enables proper display of both underexposed and overexposed areas in the image simultaneously while maintaining the natural look of the image. More testing is required to find appropriate parameters for various image types.

This thesis consider enhancement of X-ray fluoroscopy image sequences. The purpose is to investigate the possibilities to improve the image enhancement in Biplanar 500, a fluoroscopy system developed by Swemac Medical Appliances, for use in orthopedic surgery. An algorithm based on recursive filtering, for temporal noise suppression, and motion compensation, for avoidance of motion artifacts, is developed and tested on image sequences from the system. The motion compensation is done both globally, by using the theory of the shift theorem, and locally, by subtracting consecutive frames. Also a new type of contrast adjustment is presented, received with an unlinear mapping function. The result is a noise reduced image sequence that shows no blurring effects upon motion. A brief study of the result shows, that both the image sequences with this algorithm applied and the contrast adjusted images are preferred by orthopedists compared to the present images in the system.

I hälso- och sjukvården i Sverige används ett flertal medicintekniska apparater och IT-system. Ofta läser man av en skärm på den medicintekniska apparaten och noterar informationen på papper för att sedan manuellt mata in dessa uppgifter i IT-systemen. Detta manuella mellanled finns det önskemål och förhoppningar om att slippa. Man vill kunna överföra information automatiskt mellan den medicintekniska apparaten och IT-systemet, med andra ord vill man integrera dem. För att åstadkomma detta måste ett antal grund-läggande förutsättningar uppfyllas. Bland annat ställs vissa krav på tekniken hos den medicintekniska apparaten, kommunikationskanalen och IT-systemet. Dessutom är det nödvändigt att uppfylla gällande lagar och regler. Utöver detta finns det faktorer som kan underlätta, till exempel användandet av erkända standarder för kommunikationen. Syftet med detta examensarbete har varit att belysa och utreda dessa bakomliggande faktorer för att få integration av nyss beskrivet slag att fungera. Arbetet är en bakgrundsstudie på detta område.