Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.

This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.

Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard.

A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

Ultrasound is one of the most commonly used noninvasive medical imaging techniques. Ultrasound contrast agents (UCA), consisting of encapsulated gas-filled microbubbles, have shown to increase the diagnostic precision in selected low echogenic patients. UCA also holds promise for bedside evaluation of myocardial perfusion quantification, but is not yet reproducible and specific enough for clinical use. In addition risks have been addressed when used, as first recommended, together with high mechanical index (MI) for reperfusion assessment by contrast destruction. We clinically observed increased myocardial velocities after UCA-administration when applied simultaneously with color tissue Doppler imaging (CTDI) arising the question if this increase was due to physiological factors or physical changes in the backscattered signals when UCA were present.

The aims of the thesis was to explain this velocity shift and simultaneously to contribute to a future safe and contrast specific application by further characterizing the non-linear acoustic properties of UCA when located in an acoustic field. Of specific interest was to evaluate in which way nonlinear wave propagation affects the response from UCA and if a change in pulse shape, length or polarity can be utilized to increase the nonlinear signal contribution.

Twelve patients with ischemic heart disease were examined with CTDI before and after UCA-administration in order to verify the change in peak systolic velocity. An experimental in vitro model including flow and tissue phantoms for UCA was established for CTDI. Raw data from single-element transducers and clinical ultrasound systems were collected for three different UCA and analyzed to determine if the observed velocity shift could be reproduced in vitro and to find a possible cause. Our results show in vivo and in vitro that UCA will affect the autocorrelation phase shift estimator used for CTDI in terms of contribution from rupturing UCA microbubbles, which explains the velocity shift. CTDI during contrast infusion should therefore be avoided unless it can be performed at low MI where the majority of the UCA are intact.

The computational model for spatial superposition of attenuated waves was modified to include an operator for pulse distortion from nonlinear wave propagation. The Matlab™ toolbox Bubblesim based on a modified Rayleigh-Plesset-equation and with insonation parameters such as frequency, pressure amplitude, pulse length and polarity was used to study the response from single microbubbles either for simulated pulses or for pulses generated by clinical ultrasound systems and single element transducers. The combination of the two models also provided a computational platform to asses pulse distortion from nonlinear wave propagation, the response of the UCA bubble and the linear backscatter of the low amplitude bubble echo. When evaluating the harmonic response in simulations and in vitro, the interaction of the excitation pulses with the contrast bubbles was identified as the main cause of nonlinear scattering, and a 2-3 dB increase of the second harmonic amplitude depends on nonlinear distortions of the incident pulse. By applying small changes of short (<3.5 cycles) and fragmented transmitted wideband pulses of 2-2.5 MHz, it is shown that inverted pulse polarity considerably modulates power without affecting a low and safe MI (<0.4), and the results lodged promise to further to enhance a contrast response.

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 use of Electronic Health Records (EHR) is wide spread in healthcare today. EHRs are not only used to support daily care but also used to support important secondary uses, e.g. clinical research, quality assurance and education. Although considered advantageous compared to paper-based records, EHRs still have a long way to go in realizing its full potential as an integral part of a safe, effective and efficient health care system.

Making EHRs interoperable is a prerequisite to support increasingly distributed and diverse healthcare. Bringing up-to-date knowledge into EHRs for decision support is a critical step to foster evidence based care. EHR data from different sources need to be analyzed in research in order to find new evidence for improvement of the current practice. Knowledge in the form of guidelines needs to be disseminated and applied in practice through continuous education. This cyclic flow of information and knowledge between care, research and education must be facilitated in order to achieve a safer and more efficient healthcare. An interoperable EHR framework can facilitate the sharing of information and knowledge between not only human users but also participating software systems. This is the aim of this thesis, which is built upon the research in the field of semantic interoperability, in particular the pioneering work by the openEHR Foundation.

The journey of this thesis started with a template-based supplementary EHR system - Julius, which allows clinicians to define and share record structures for care and research. The formalism behind Julius is comparable to the openEHR archetype formalism but less expressive and without the backing of international standards. This finding led to an open source implementation of the openEHR design, which in turn initiated the validation and further improvements of the archetype formalism. The software components made the archetype formalism more accessible to academic and commercial projects around the world.

The investigation of the convertibility between a legacy EHR content model and the archetype model showed that the archetype format is more expressive and thus can be used to preserve legacy EHR content definitions. A general strategy for migration from legacy EHRs to archetype-based EHRs was formulated. A novel way of representing clinical practice guidelines using archetype formalism was proposed and tested on a lymphoma chemotherapy guideline. The implication of this study is improved interoperability between guidelines and EHRs that could facilitate both clinical decision support and guideline-compliance checking. Maintainability of guidelines could be increased through reuse of EHR content models as building blocks of guidelines. In the last part of the research, a way of expressing fully structured care plans using openEHR and CONTsys has been explored based on the requirements for elderly home care. A sharable and semantically well-defined care plan could contribute to the coordination of shared care.

The key contribution of the thesis can be summarized as the validation and further improvement of the openEHR archetype formalism through software implementation and the explorations on clinical guidelines, shared care plans and legacy EHR content models in relation to archetypebased EHR framework.

Laser Doppler flowmetry (LDF) is virtually the only non-invasive technique, except for other laser speckle based techniques, that enables estimation of the microcirculatory blood flow. The technique was introduced into the field of biomedical engineering in the 1970s, and a rapid evolvement followed during the 1980s with fiber based systems and improved signal analysis. The first imaging systems were presented in the beginning of the 1990s.

Conventional LDF, although unique in many aspects and elegant as a method, is accompanied by a number of limitations that may have reduced the clinical impact of the technique. The analysis model published by Bonner and Nossal in 1981, which is the basis for conventional LDF, is limited to measurements given in arbitrary and relative units, unknown and non-constant measurement volume, non-linearities at increased blood tissue fractions, and a relative average velocity estimate.

In this thesis a new LDF analysis method, quantitative LDF, is presented. The method is based on recent models for light-tissue interaction, comprising the current knowledge of tissue structure and optical properties, making it fundamentally different from the Bonner and Nossal model. Furthermore and most importantly, the method eliminates or highly reduces the limitations mentioned above.

Central to quantitative LDF is Monte Carlo (MC) simulations of light transport in tissue models, including multiple Doppler shifts by red blood cells (RBC). MC was used in the first proof-of-concept study where the principles of the quantitative LDF were tested using plastic flow phantoms. An optically and physiologically relevant skin model suitable for MC was then developed. MC simulations of that model as well as of homogeneous tissue relevant models were used to evaluate the measurement depth and volume of conventional LDF systems. Moreover, a variance reduction technique enabling the reduction of simulation times in orders of magnitudes for imaging based MC setups was presented.

The principle of the quantitative LDF method is to solve the reverse engineering problem of matching measured and calculated Doppler power spectra at two different source-detector separations. The forward problem of calculating the Doppler power spectra from a model is solved by mixing optical Doppler spectra, based on the scattering phase functions and the velocity distribution of the RBC, from various layers in the model and for various amounts of Doppler shifts. The Doppler shift distribution is calculated based on the scattering coefficient of the RBC:s and the path length distribution of the photons in the model, where the latter is given from a few basal MC simulations.

When a proper spectral matching is found, via iterative model parameters updates, the absolute measurement data are given directly from the model. The concentration is given in g RBC/100 g tissue, velocities in mm/s, and perfusion in g RBC/100 g tissue × mm/s. The RBC perfusion is separated into three velocity regions, below 1 mm/s, between 1 and 10 mm/s, and above 10 mm/s. Furthermore, the measures are given for a constant output volume of a 3 mm³ half sphere, i.e. within 1.13 mm from the light emitting fiber of the measurement probe.

The quantitative LDF method was used in a study on microcirculatory changes in type 2 diabetes. It was concluded that the perfusion response to a local increase in skin temperature, a response that is reduced in diabetes, is a process involving only intermediate and high flow velocities and thus relatively large vessels in the microcirculation. The increased flow in higher velocities was expected, but could not previously be demonstrated with conventional LDF. The lack of increase in low velocity flow indicates a normal metabolic demand during heating. Furthermore, a correlation between the perfusion at low and intermediate flow velocities and diabetes duration was found. Interestingly, these correlations were opposites (negative for the low velocity region and positive for the mediate velocity region). This finding is well in line with the increased shunt flow and reduced nutritive capillary flow that has previously been observed in diabetes.