Licentiatavhandling Ronneby : Blekinge tekniska högskola, 2007.

<p>In inhalation anesthesia life-support systems are used to provide gases to the patient and to monitor important parameters. These systems are used in dynamic and stressful situations where the information-load on the operators often is significant. This thesis presents a design concept of an anesthetic system. The design goal has been to keep the information-load on the operator low with the objective to minimize confusion and error.</p><p>A number of contextual inquiries were made in the operating room during surgery at four hospitals in Sweden. The goal was to understand the actual use-situations when anesthetic systems are employed. In addition to these inquiries a number of interviews with clinicians were made. The result from the contextual inquiries and the interviews was a set of qualities-in-use; characterizations of the preferred use-qualities of the system. Based on these qualities-in-use the actual design concept was developed. The design concept is described in terms of sketches, scenarios and the qualities-in-use on which it was based. The thesis also includes a discussion on the advantages and disadvantages of the design concept.</p>

<p>KTH, Division of Neuronic Engineering, have a finite element model of the head. However, this model does not contain detailed modeling of the brain. This thesis project consists of finding a method to extract brain tissues from T1-weighted MR images of the head. The method should be automatic to be suitable for patient individual modeling.</p><p>A summary of the most common segmentation methods is presented and one of the methods is implemented. The implemented method is based on the assumption that the probability density function (pdf) of an MR image can be described by parametric models. The intensity distribution of each tissue class is modeled as a Gaussian distribution. Thus, the total pdf is a sum of Gaussians. However, the voxel values are also influenced by intensity inhomogeneities, which affect the pdf. The implemented method is based on the expectation-maximization algorithm and it corrects for intensity inhomogeneities. The result from the algorithm is a classification of the voxels. The brain is extracted from the classified voxels using morphological operations.</p>

No description available.

<p>I dagens samhälle är det vanligt att patienter söker vård hos olika vårdgivare, vilket resulterar i att vårdinformationen blir splittrad och allt högre krav ställs på de datoriserade journalsystemen. Till exempel ska de stödja ett processorienterat arbetssätt för att samla information från samma vårdprocess. Vårdinformationen måste därför vara tillgänglig över organisationsgränser och olika journalsystem måste kunna kommunicera med varandra, där en ökad tillgänglighet ställer högre krav på informationssäkerhet och behörighetskontroll. Journalsystemen bör, för att uppfylla dessa krav, utvecklas enligt standarder och riktlinjer.</p><p>Det finns ett flertal nationella och internationella organisationer som arbetar med att ta fram standarder och riktlinjer för hur journalsystem bör utvecklas. Problemet är att dessa arbeten sker på olika nivåer och gäller olika delar av journalsystemen. Det är därmed en stor utmaning för journalleverantörer att förhålla sig till dessa arbeten. Syftet med det här examensarbetet har varit att utreda hur utvalda, svenska och europeiska, standardiseringsarbeten förhåller sig till varandra samt att avgöra på vilket sätt de är av relevans för journalleverantörer. Dessutom har syftet varit att framställa en prototyp av ett IT-stöd för processorienterad vårddokumentation.</p><p>En kvalitativ litteraturstudie har i det här examensarbetet resulterat i en sammanställning av de utvalda standardiseringsarbetena. Arbetena hanterar främst områden som kan användas för att ena vårdprocessen, exempel på dessa är behörighetskontroll och standardiserad kommunikation med informationsspecifikationer eller arketyper. SAMBA har tagit fram en processmodell som beskriver vårdprocessen. Baserat på denna modell och krav från standardiseringsarbetena har vi framställt ett förslag på ett IT-stöd för processorienterad vårddokumentation.</p>

<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.</p><p>The results of this work are;</p><p>1. a parameter setting tool that makes the initial parameter setting much easier and</p><p>2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.</p>

<p>Magnetic Resonance Imaging is an established technology for both imaging and</p><p>functional studies in clinical and research environments. The field is still very</p><p>research intense. Two major research areas are acquisition time and signal quality.</p><p>The last decade has provided tools for more efficient possibilities of trading these</p><p>factors against each other through parallel imaging.</p><p>In this thesis one parallel imaging method, Sensitivity Encoding for fast</p><p>MRI (SENSE) is examined. An alternative solution CCASENSE is developed.</p><p>CCASENSE reduces the acquisition time by estimating the sensitivity maps required</p><p>for SENSE to work instead of running a reference scan. The estimation</p><p>process is done by Blind Source Separation through Canonical Correlation Analysis.</p><p>It is shown that CCASENSE appears to estimate the sensitivity maps better</p><p>than ICASENSE which is a similar algorithm.</p>

<p>Technology of today makes it possible to change the way traditional healthcare is conducted. As the population grows older, and the elderly is becoming an increasing part of the whole population, the need for cost efficient and personalised care increases. By implementing home healthcare IT projects, it is possible for more patients to be treated at home with sustained quality of care.</p><p>This thesis documents the work of a master’s degree project carried out during the autumn of 2005. The project is part of a research project within the Department of Biomedical Engineering at Linköping University. The project aims at developing a data model for storing medical data. The model should be general and easy to expand. This model is intended to be used within a larger system allowing a patient to measure medical data from a remote location.</p>

Immunoglobulin E (IgE) mediated allergy is a major and seemingly increasing health problem in the Western countries. The combined usage of databases of molecular and clinical information on allergens (allergenic proteins) as well as new experimental platforms capable of generating huge amounts of allergy-related data from a single blood test holds great potential to enhance our knowledge of this complex disease. To maximally benefit from this development, however, both novel and improved methods for computational analysis are urgently required. This thesis concerns two types of important and practical computational analyses of allergens: allergenicity/IgE-cross-reactivity risk assessment and characterization of IgE-reactivity patterns. Both directions rely on development and implementation of bioinformatics and statistical learning algorithms, which are applied to either amino acid sequence information of allergenic proteins or on quantified human blood serum levels of specific IgE-antibodies to allergen preparations (purified extracts of allergenic sources, such as e.g. peanut or birch). The main application for computational risk assessment of allergenicity is to prevent unintentional introduction of allergen-encoding transgenes in genetically modified (GM) food crops. Two separate classification procedures for potential protein allergenicity are introduced. Both protocols rely on multivariate classification algorithms that are educated to discriminate allergens from presumable non-allergens based on their amino acid sequence. Both classification procedures are thoroughly evaluated and the second protocol shows state-of-the-art performance in comparison to current top-ranked methods. Moreover, several pitfalls in performance estimation of classifiers are demonstrated and procedures to circumvent these are suggested. Visualization and characterization of IgE-reactivity patterns among allergen preparations are enabled by application of bioinformatics and statistical learning methods to a multivariate dataset holding recorded blood serum IgE-levels of over 1000 sensitized individuals, each measured to 89 allergen preparations. Moreover, a novel framework for divisive hierarchical clustering including graphical representation of the resulting output is introduced, which greatly simplifies analysis of the abovementioned dataset. Important IgE-reactivity relationships within several groups of allergen preparations are identified including well-known groups of clinically relevant cross-reactivities.

Magnetic Resonance Imaging is an established technology for both imaging and functional studies in clinical and research environments. The field is still very research intense. Two major research areas are acquisition time and signal quality. The last decade has provided tools for more efficient possibilities of trading these factors against each other through parallel imaging. In this thesis one parallel imaging method, Sensitivity Encoding for fast MRI (SENSE) is examined. An alternative solution CCASENSE is developed. CCASENSE reduces the acquisition time by estimating the sensitivity maps required for SENSE to work instead of running a reference scan. The estimation process is done by Blind Source Separation through Canonical Correlation Analysis. It is shown that CCASENSE appears to estimate the sensitivity maps better than ICASENSE which is a similar algorithm.