In this project the possibility of using a network of magnetometers sensing a permanent magnet for tracking has been investigated. Both the orientation and the position of the magnet have been considered. A dipole approximation of the magnetic field is used to develop two models. One of the models parametrizes the orientation with the magnetic moment vector, while the other parametrizes the orientation with a unit quaternion. An extended Kalman filter have been used to estimate position and orientation.

Several calibration algorithms have been developed to calibrate for sensor errors, differences in sensor coordinate frame orientations and also for the estimation of the magnetic moment norm of a permanent magnet. The models have been tested using an optical reference system for position and orientation estimation. Initial results are ambiguous and further testing is necessary. One conclusion is that the model using the magnetic moment vector as orientation parametrization is less sensitive to the accuracy of the initial guesses of the filter recursions and also less sensitive to possible model errors.

A mathematical result of the possibility of using a non stationary sensor network to track the magnet is also given.

The fabrication of stretchable conductive material through vapor phase polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT) is presented alongside a method to easily pattern these materials with nanosecond laser structuring. The devices were constructed from sheets of vapor phase polymerized PEDOT doped with tosylate on pre-stretched elastomeric substrates followed by laser structuring to achieve the desired geometrical shape. Devices were characterized for electrical conductivity, morphology, and electrical integrity in response to externally applied strain. Fabricated PEDOT sheets displayed a conductivity of 53.1 ± 1.2 S cm−1; clear buckling in the PEDOT microstructure was observed as a result of pre-stretching the underlying elastomeric substrate; and the final stretchable electronic devices were able to remain electrically conductive with up to 100% of externally applied strain. The described polymerization and fabrication steps achieve highly processable and patternable functional conductive polymer films, which are suitable for stretchable electronics due to their ability to withstand externally applied strains of up to 100%.

The aim of the project was to compare the workflow for an Elekta Linac with and without the surfacescanning system Catalyst and describe pros and cons with both workflows. The findings in the reportcan be used as decision support in development of Elekta products and workflow improvements.

The method for the project was to do interviews, observations and time measurements at Södersjukhuset(not using Catalyst) and Sundsvalls sjukhus (using Catalyst). The workflows were graded in an as-sessment protocol covering time efficiency, comfort, noise, resources, reliability, cost, dosage and sideeffects. Different workflow scenarios were simulated in AnyLogic.

The result of the project was that, according to our protocol, the workflow with Catalyst was ratedhigher than without it. The simulations in Anylogic showed that minimizing gaps in the treatment sched-ule generated the same number of patients treated per day, if the positioning could not be done faster.The simulations also showed that removing position verification with cone beam computer tomography(CBCT), an imaging system which is used in addition to the Catalyst system, would increase the numberof treated patients with approximately 33%.

The conclusion was that there were no great differences in time efficiency between the workflows. How-ever, considering the higher reliability and comfort for the patient, optical surface scanning can improvethe positioning for Elekta Linac and is therefore worth implementing. Minimizing treatment gaps wouldnot improve the workflow. Removing the use of CBCT would increase the number of treated patientsper day.

In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving  PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project. 

In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance. 

This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R² of 0.68 and an MAE of 1.51 days compared to RF's R² of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are necessary for comparable results with maintained predictive accuracy. Furthermore, Principal Component Analysis (PCA) reveals that these informative metabolites are primarily located within a specific cluster on the first and second principal components (PC), suggesting a need for further research into the biological context of these metabolites. 

In conclusion, the dataset has proven valuable for predicting PMI. This indicates significant potential for employing machine learning models in PMI estimation, thereby assisting forensic pathologists in determining the time of death. Notably, the model shows promise in surpassing current methods and filling crucial gaps in the field, representing an important step towards achieving accurate PMI estimations in forensic practice. This project suggests that machine learning will play a central role in assisting with determining time since death in the future. 

A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS.

Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.

Background: Voice is still the most important tool used by teachers, but voice is varied and subject to problems. Teachers who lecture for long hours or have taught for most of their professional lives, face issues such as voice discomfort, chronic voice disorders, occupational voice disorder, stress, physiological and psychological problems. Due to issues such as poor acoustics in the classroom and absence of any installed voice amplification system, teachers might be subject to increase their vocal load to reach all the students in the classroom.

Purpose: The aim of this study is to study the fundamental frequency (f_o) and the strength L_eq[dB(C)] of the teacher’s voice, with and without voice amplification system. 

Method: Students from the preparatory year of their studies at KTH (CBH), were asked if they would like to participate in this study that researches on the voice health of teacher. Two days of teaching sessions were recorded using a microphone attached to the teacher’s mouth and three other static microphones placed in different parts of the room. The voice amplification system used in the study, was the one that was already in use at some of the classrooms at KTH. The students and the participating teacher were given questionnaires, towards the end of the teaching session to voice their opinion on the teaching voice, with and without the voice amplification system. 

Results: The results from the data collected from the recordings, have shown that the SPL increased between 0.6 dB(C) and 5 dB(C) when the voice amplification system was turned on. The fundamental frequency (f_o) of the teacher’s voice reduced by 1.4% with the use of the voice amplifier. The response of the students was overwhelmingly in support of the voice amplification system used during the recordings. 60-62% of female respondents and 68-79% of male respondents felt that the voice amplification system increased the audibility in the classroom.

Conclusion: L_eq [dB(C)] values from the mics around the classroom showed a considerable increase and thus the amplification system provides a good acoustic condition for the teacher to communicate with his students and a significant improvement in student’s comprehension of the teacher’s voice and learning conditions. There was a decrease in the f_o of the teacher’s voice with the amplification, which is good for the teacher’s vocal health and reduces voice disorders while improving the quality and audibility of teacher’s voice. Majority of the students felt that the amplification system was beneficial during the teaching sessions and would like to see more amplifiers installed in more classrooms.

Samverkan kan leda till innovation, konkurrenskraftiga företag, förstklassig forskning samt välfungerande myndigheter och institutioner. I den politiska debatten idag finns en förväntan att Sverige ska upprätthålla sin konkurrenskraft och bemöta samhällets utmaningar genom innovation och att vägen till innovation går via samverkan. Avhandlingen bygger på en studie av ett samverkansprojekt vars syfte var att skapa innovation för att minska antalet vårdrelaterade infektioner i Sverige. Projektet som studerats ses som en transdisciplinär ansats med aktörer som representerade akademi, industri samt hälso- och sjukvård.

Syftet med avhandlingen är att vidareutveckla kunskapen om interorganisatorisk samverkan för innovation. Detta görs genom ett tredelat bidrag, till teoribildningen kring samverkan för innovation som börjat växa fram, till den samverkande praktiken inom både privat och offentlig sektor samt till politiker och beslutsfattare som styr fördelning av statliga anslag till forskning och innovation.

Fallstudien som ligger till grund för avhandlingen är baserad på en etnografiskt inspirerad studie. Empiriskt material samlades in och skapades tillsammans med aktörerna i projektet under drygt två års tid genom intervjuer och deltagande observation.

Studien visar att interorganisatorisk samverkan består av flera dimensioner och kan förstås på flera nivåer. Interorganisatorisk samverkan innebär inte bara att det är olika organisationer som ska göra en gemensam ansträngning. Organisationerna består av olika människor med olika discipliner och professioner vilka bygger på olika utgångspunkter och sätt att se på världen. Samverkan kan ses som ett sätt att fylla mellanrummen mellan organisationer istället för att bygga broar över gränser. I de organisatoriska mellanrummen kan aktörer från olika organisationer, med olika discipliner och professioner mötas utan institutionaliserade roller, i en receptiv kontext där innovation kan skapas.

One of the current topics of research in neuroimaging techniques is related to explaining and modelling the Blood Oxygen Level Dependent (BOLD) responses. BOLD responses are estimated by processing functional Magnetic Resonance Imaging (fMRI) data. BOLD responses are caused by hemodynamic responses to neural activity which alter the levels of blood oxygenation at local brain regions. The main aims of the current thesis were to i) develop and examine methods regarding BOLD response estimation from the visual cortex and the frontal cortex of human brain and to ii) develop a model in order to explain the physiological mechanisms which cause the estimated BOLD responses.

In order to satisfy the main aims, fMRI data were provided by the Center of Medical Imaging and Visualization (CMIV). The provided fMRI data consist of fMRI brain measurements of twelve healthy human subjects who were subjected to visual stimulation. By processing the fMRI data, Regions Of Interest (ROIs) were extracted at the anatomical sites of the visual cortex and the frontal cortex. Afterwards, the fMRI data were manipulated in order to extract BOLD responses from the visual cortex and the frontal cortex. Various methods were developed and compared in terms of which technique provided well representative BOLD responses.       

Subsequently, a model was developed by using software Wolfram Mathematica 9 in order to explain the physiological mechanisms of the estimated BOLD responses at the visual and the frontal cortex. The model aimed to solve for oxygen concentration in blood plasma as blood flows from the arterial part to the venous part of the blood circulation system through a capillary. Oxygen outward diffusion through the capillary wall and oxygen concentration at the extravascular environment were modelled as well. Blood plasma oxygen concentration was turned into hemoglobin oxygen saturation (Sa ) through hemoglobin oxygen dissociation curve and Henry’s law for gases. As a result, the Sa  was estimated through modelling for oxygen concentration in blood plasma. Finally, the developed model ended to a system with input the fractional change of Cerebral Blood Flow (CBF) velocity and Cerebral Metabolic Rate of Oxygen (CMR ) and as output a proportional signal to the BOLD response. By simulating for different scenarios of fractional changes of CBF velocity and CMR  and by comparing the resulted BOLD responses to the estimated ones, it was attempted to explain for the physiological mechanisms which caused the BOLD responses at the anatomical sites of the visual and frontal cortex.

Due to the expected mass deployment of millimeter-wave wireless technologies, thresholds of potential millimeter-wave-induced biological and health effects should be carefully assessed. The main purpose of this study is to propose, optimize, and characterize a near-field exposure configuration allowing illumination of cells in vitro at 60 GHz with power densities up to several tens of mW/cm(2) . Positioning of a tissue culture plate containing cells has been optimized in the near-field of a standard horn antenna operating at 60 GHz. The optimal position corresponds to the maximal mean-to-peak specific absorption rate (SAR) ratio over the cell monolayer, allowing the achievement of power densities up to 50 mW/cm(2) at least. Three complementary parameters have been determined and analyzed for the exposed cells, namely the power density, SAR, and temperature dynamics. The incident power density and SAR have been computed using the finite-difference time-domain (FDTD) method. The temperature dynamics at different locations inside the culture medium are measured and analyzed for various power densities. Local SAR, determined based on the initial rate of temperature rise, is in a good agreement with the computed SAR (maximal difference of 5%). For the optimized exposure setup configuration, 73% of cells are located within the ±3 dB region with respect to the average SAR. It is shown that under the considered exposure conditions, the maximal power density, local SAR, and temperature increments equal 57 mW/cm(2) , 1.4 kW/kg, and 6 °C, respectively, for the radiated power of 425 mW.

Diagnosing abnormal fetal cardiac function using ultrasound is a complicated procedure which makes it difficult to obtain high quality results from ultrasound examinations that are performed shortly before delivery. Color tissue Doppler imaging (cTDI) is the echocardiographic technique that has been used to obtain the data for this project. Subtle changes in the fetal cardiac function caused by a variety of complications can possibly be detected using cTDI. Fetuses suffering from these complications are often involved in high-risk deliveries. Combining the data obtained from cTDI with Artificial Intelligence (AI) may improve precision and accuracy when it comes to diagnosing pathological conditions involving fetal cardiac function before delivery. AI uses machines to perform and execute tasks that are characteristic of human intelligence. AI can be achieved by using deep learning. Deep learning uses algorithms called artificial neural networks that are inspired by the biological structure and function of the human brain. The neural networks classify information in a similar manner to the human brain. A platform that uses deep learning can make statements or predictions based on the data fed to it. The AI platform Peltarion uses deep learning to perform tasks. The aim of this project was to use Peltarion to evaluate the possibility of predicting high-risk deliveries with abnormal perinatal outcome by using data obtained by cTDI velocity recordings of the fetal heart. The data included myocardial velocity recordings from 107 pregnancies, out of the 107 pregnancies 82 of the babies were born healthy while 25 babies had an adverse perinatal outcome. The data was uploaded in the platform and three models were built and trained in order to evaluate the performance of the platform using the data. The parameters that have been used to determine the results are loss, accuracy and precision. The results showed that the accuracy parameter was measured to be 0.8 in all cases which means that the model correctly predicts if a fetal heart is healthy or likely to have an adverse outcome 80% of the time. The precision parameter was measured to be around 0.4 which means out of all the times the model predicted a fetal heart to have an adverse outcome, only 40% truly had an adverse outcome. It was concluded that a substantially larger amount of evenly distributed data is required to appropriately evaluate the possibility of using fetal myocardial velocity recordings as data for the AI platform Peltarion to predict high-risk deliveries.

This work aims at understanding the state of the art in the broad contextual research area of "medical concept representation". Our data support the general understanding that the focus of research has moved toward medical ontologies, which we interpret as a paradigm shift. Both the opinion of socially active groups of researchers and changes in bibliometric data since 1988 support this opinion. Socially active researchers mention the OBO foundry, SNOMED CT, and the UMLS as anchor activities.

This report presents an evaluation of a centrifugal air purifier from the company Airission used in an infectious ward at Karolinska University Hospital. Airission's air purifier removes particles and aerosols using centrifugal technology, a relatively untested technology for air purification. The goal was to investigate and attempt to verify the functionality and performance of the air purifier and compare it to a conventional air purifier that uses traditional two-stage filters for air purification.To conduct the study, a bioaerosol measuring instrument was used to measure real-time particle levels in the room. The tests were performed under different operating conditions and time intervals with the air purifier turned on and off. Data collection and analysis included calculating the mean values, comparing the particle levels between different test cases, and calculating the standard deviation.The results showed that Airission's centrifugal air purifier effectively purifies air from particles and aerosols. The purification efficiency was comparable to a conventional air purifier. It was more effective than a conventional air purifier without the use of highefficiency air filters, commonly known as HEPA filters. A significant reduction in the number of particles in the air was observed while the air purifier was in operation. However, some complications arose during the application of the HEPA filter, which could have had a negative impact on both air purifiers. In summary, the analysis demonstrates that the applied centrifugal technology in Airission's air purifier works well. The comparison with the conventional air purifier shows certain advantages of an air purifier that uses centrifugal technology - in terms of both efficiency and quality. This report contributes to the knowledge of air purification solutions to improve air quality and reduce the spread of airborne diseases, especially in hospital environments. The results can be useful for further research and development of more effective air purifiers, which in turn provide better protection for patients and hospital staff exposed to airborne pathogens.

Intelligent Environments on varying scales and for different purposes are slowly becoming a reality. In the near future, global smart world infrastructures will become a commodity that will support various activities of daily life at different degrees of realism. Such infrastructures have the potential to offer dedicated, context- and situation-aware information and services by simultaneously providing the next-generation of data collection, execution and service provisioning layers. One key aspect of this vision is the correct monitoring and understanding of how people interact with their environment; how they can actually benefit from the added intelligence; and finally how future services can be improved or better personalized to enhance human environment interaction as a whole. This level of intelligence is of particular relevance in the health and social care domain where person-centric services can be deployed to assist or even enable a person in performing activities of daily living. This paper discusses the concept of embedded self-aware profiles for smart devices that can be used to gain a deeper contextual understanding of their use and also discusses the emergence of a general model of Ambient Intelligence that is based on the collective existence and behavior of such smart devices. Although generic in principle, the proposed concepts have been exemplified by a distinct use case, namely a smart kettle.

Objective: An imaging device to locate functionalised nanoparticles, whereby therapeutic agents are transported from the site of administration specifically to diseased tissues, remains a challenge for pharmaceutical research. Here, we show a new method based on electrical impedance tomography (EIT) to provide images of the location of gold nanoparticles (GNPs) and the excitation of GNPs with radio frequencies (RF) to change impedance permitting an estimation of their location in cell models Methods: We have created an imaging system using quantum cluster GNPs as contrast agent, activated with RF fields to heat the functionalized GNPs, which causes a change in impedance in the surrounding region. This change is then identified with EIT. Results: Images of impedance changes of around 80 ± 4% are obtained for a sample of citrate stabilized GNPs in a solution of phosphate-buffered saline. A second quantification was carried out using colorectal cancer cells incubated with culture media, and the internalization of GNPs into the colorectal cancer cells was undertaken to compare them with the EIT images. When the cells were incubated with functionalised GNPs, the change was more apparent, approximately 40 ± 2%. This change was reflected in the EIT image as the cell area was more clearly identifiable from the rest of the area. Significance: EIT can be used as a new method to locate functionalized GNPs in human cells and help in the development of GNP-based drugs in humans to improve their efficacy in the future.

The requirement of always obtaining participants' informed consent in research with human subjects cannot always be met, for a variety of reasons. This paper describes and categorises research situations where informed consent is unobtainable. Some of these kinds of situations, common in biomedicine and psychology, have been previously discussed, whereas others, for example, those more prevalent in infrastructure research, introduce new perspectives. The advancement of new technology may lead to an increase in research of these kinds. The paper also provides a review of methods intended to compensate for lack of consent, and their applicability and usefulness for the different categories of situations are discussed. The aim of this is to provide insights into one important aspect of the question of permitting research without informed consent, namely, how well that which informed consent is meant to safeguard can be achieved by other means.

At CBH (The School of Engineering Sciences in Chemistry, Biotechnology and Health) a micro-CT has been developed, but the system has potential improvement regarding the usability. The purpose of this bachelor’s thesis was to improve the software by automatizing three aspects of manual data handling: Firstly to place the data in a folder with a name related to the obtained acquisition settings. Secondly to transfer files through SCP between the two computers connected to the micro-CT. Lastly to delete the copied data from the disk that initially saves the files.

In order to automate the data transfer an SSH key pair with public key authentication was implemented between the two computers. To the existing software a script as well as a class controlling the three automation aspects were added. From the user interface, the class initially retrieves information about the acquisition settings to the folder name and when the acquisition is complete, the program code calls the script file that performs the SCP transfer. Afterwards a verification is made that no data was lost during the transfer, after which a deletion of the copied files is performed on the original disk. The new class improves the usability of the micro-CT and facilitates the handling of data after an acquisition.

Research on photon counting detectors (PCDs) is focused on semiconductor materials, and silicon is a strong candidate to use in PCDs for photon counting computer tomography (CT). In a silicon detector, a significant portion of the counts is due to Compton scattering events. Since only part of the incident photon energy is deposited in a Compton interaction, Compton interactions lead to a loss of spectral information. By using Compton coincidence detection, i.e., combining information from multiple Compton events caused by the same incident photon, it is possible to obtain more spectral information from Compton scattered photons, increasing the energy resolution of the detector. The goal of this thesis is to develop and evaluate a method for Compton coincidence detection for photon counting CT.

In this thesis, a method for Compton coincidence detection based on Compton kinematics and a χ² test is presented and compared to a previously developed method based on maximum likelihood estimation. The χ² method utilised the connection between the energy before vs after a Compton interaction, and the scattering angle. The possible scattering angles due to deposited energy in each interaction were called the energy angles. The spatial angles between the interaction positions in the detector were calculated and compared to the energy angles through a χ² test in order to find the correct order of interaction and the incident photon energy. The χ² method correctly identified the interaction order of 85.8% of simulated interaction chains ending in photoelectric effect and 64.1% of simulated interaction chains containing only Compton interactions. The energy estimation was 100% correct for all chains ending in photoelectric effect, since all of the incident energy was deposited in the detector. For chains of only Comptoninteractions, the energy was estimated with an RMS error of 21.2 keV. Combining the results from chains ending in a photoelectric interaction and chains of only Comptoninteractions, the total RMS error of the energy estimation was 11.5 keV. 

Semiconductor detectors are attracting interest for use in photon-counting spectral computed tomography. In order to obtain a high spatial resolution, it is of interest to find the photon interaction position. In this work we investigate if machine learning can be used to obtain a sub-pixel spatial resolution in a photon-counting silicon strip detector with pixels of 10 µm. Simulated charge distributions from events in one, three, and seven positions in each of three pixels were investigated using the MATLAB® Classification Learner application to determine the correct interaction position. Different machine learning models were trained and tested in order to maximize performance. With pulses originating from one and seven positions within each pixel, the model was able to find the originating pixel with an accuracy of 100% and 88.9% respectively. Further, the correct position within a pixel was found with an accuracy of 54.0% and 29.4% using three and seven positions per pixel respectively. These results show the possibility of improving the spatial resolution with machine learning.

A basic principle for Needfinding [Faste, 1987; Patnaik & Becker, 1999] is that designers and engineers should interact directly with users to get direct insights into the user domain. Needfinding is not a new phenomena, it is almost forty years ago since the process was adopted at Stanford University’s product design program [Patnaik & Becker, 1999]. As the name, Need-finding, implies, this is an intertwined approach to find needs which are not readily articulated by users. The application of a Needfinding process offers qualitative methods to make those needs visible early on in product development. In fact, the process has become more interesting during recent time, since qualitative methods have gained more acceptance outside the academic realm [ibid.].The word qualitative indicates that what are sought for are qualities such as people’s experiences, what they perceive or interpret into a situation [Miles & Huberman, 1994; Patton, 2002]. Such data is contextually dependent, i.e., it must be generated in the context in which the phenomena occur. Besides context, people’s activities, behaviours and goals are important to observe and learn from. The objectives, for applying Needfinding, are to make the identification of needs and design a seamless effort, as well as an interest to identify opportunities to innovations. Needs last longer than any solution [Patnaik & Becker, 1999], since they are grounded in people’s activities. The solution and product that might meet such needs change over time. One example is how to store computer data, the products which satisfy the need has changed from, e.g., punch cards, magnetic tape, floppy discs [ibid.] to USB-flash memories. A guiding methodology in Needfinding is a flexible process, which is adapted to the task at hand [Kelley, 2001]. Such a process is conveyed in a few basic steps and, builds on a ‘philosophy’ which permeates all activities in order to adapt the process according to the project. Therefore, the designer’s ability to rely on such a process depends on familiarity with a number of methods for observations and interviews, as well as an aptitude for socio-technical skills. Hence, the purpose in this paper is to present and reflect on methods used in a running development project to identify needs in a product development project. This is done to contribute to the advancement of a need driven product development process. The disposition of this paper is as follows. First, our approach in studying the need identification activities is presented. Second, a theoretical frame for need identification and design is presented, i.e., Needfinding [Patnaik & Becker, 1999]. Third, the practice of finding needs is outlined and discussed.

A stroke is caused by interruption of the blood supply to the brain. Yearly 15 million people around the globe endure a stroke and the costs and suffering for the people involved and the society are immense. The aim of this thesis was to investigate the response to oxygen deprivation in neurons from the medial preoptic nucleus (MPN) that have a high abundance of neuroglobin. The long term goal is to investigate the neuroprotective role of the protein in relation to stroke. Initially, the electrophysiological response of neurons to hypoxic exposure in an open system was assessed with a conventional patch-clamp setup. The first aim was to see how well the conventional system worked and if it needed improvement. Secondly, the MPN had never been investigated regarding oxygen, deprivation; hence the electrophysiological response under hypoxia needed to be investigated. The conventional patch-clamp system only allowed a reduction of the oxygen content to a level of 3-6% but not total control of the cell environment. The medial preoptic neurons showed mainly an increase of their resting membrane potential at hypoxia. The voltage activated Ca2+ and K+ currents displayed a clear attenuation when cells were subjected to hypoxia. Non-L-type Ca2+ channels were affected by hypoxic exposure and one cell indicated participation of Ca2+ activated K+ channels. However, a response could only be seen in approximately fifty percent of the neurons in the open system. This may have been due to the fact that full control of the oxygen around the neurons at hypoxia could not be achieved. A new system with full control of the ambient oxygen had to be developed in order to investigate this. After the conclusions of the first experiments, a system was developed were a labon- a-chip system was combined with the patch-clamp technique. A microfluidic system with a patch-clamp micropipette integrated was combined with optical tweezers for 3D maneuvering of the neurons. The development of patch-clamp in combination with a microfluidic system and optical tweezers allowed for full oxygen control. The experiments showed that the electrophysiological measurements were not affected by the laser when an infrared laser was used. The microfluidic system allowed very good oxygen control reaching levels of 0.5-1.5 % compared to 3-6 % in the open system. In summary, this work suggests that high voltage activated Ca2+ channels, and K+ channels are involved in the hypoxic depolarization of medial preoptic neurons. Full control of ambient oxygen in cell vicinity could be achieved by the combination of microfluidics, patch-clamp and optical tweezers. The results can be used in future studies to better understand the reaction of the brain to oxygen deprivation caused by stroke.

Hemoproteins usually supply cells and tissue with oxygen. A new hemoprotein mainly present in nerve cells called Neuroglobin was recently discovered. Enhanced expression of the protein has been shown to reduce hypoxic neural injury but the mechanism behind this function remains unknown. Methods enabling investigation of the protein in single functional neurons need to be developed. Here, we have studied how the electrical signaling capacity of a neuron was affected by hypoxic environments. Preliminary results show a trend of higher noise-level when a neuron is exposed to hypoxic compared to normoxic surroundings, which implies increased ion-channel activity. The setup used today shows shortages such as reduced control over the oxygen content due to leakage. Therefore, a gas-tight, multifunctional microfluidic system is under development which enables us to study influences of Neuroglobin concentrations on neuronal activity during hypoxia and anoxia. For electrophysiological recordings a patch-clamp micro pipette will be molded into the walls of the microfluidic system. A single biological cell is steered towards the pipette and attached there by means of optical tweezers. The Neuroglobin oxygen binding state will be studied using optical spectroscopy and the neuron environment will be manipulated by applying flows of varying oxygen content through the microfluidic system. This system will constitute a powerful tool in the investigation of the Neuroglobin mechanism of action.

It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.

Optical imaging is crucial for addressing fundamental problems in all areas of life science. With the use of confocal and two-photon fluorescence microscopy, complex dynamic structures and functions in a plethora of tissue and cell types have been visualized. However, the resolution of classical' optical imaging methods is poor due to the diffraction limit and does not allow resolution of the cellular microcosmos. On the other hand, the novel stimulated emission depletion (STED) microscopy technique, because of its targeted on/off-switching of fluorescence, is not hampered by a diffraction-limited resolution barrier. STED microscopy can therefore provide much sharper images, permitting nanoscale visualization by sequential imaging of individual-labelled biomolecules, which should allow previous findings to be reinvestigated and provide novel information. The aim of this review is to highlight promising developments in and applications of STED microscopy and their impact on unresolved issues in biomedical science.

A tightly focused pulsed laser beam can locally modify the crystal structure inside the bulk of a scintillator. The result is incorporation of so-called optical barriers with a refractive index different from that of the crystal bulk, that can be used to redirect the scintillation light and control the light spread in the detector. We here systematically study the scintillation light transport in detectors fabricated using the laser induced optical barrier technique, and objectively compare their potential performance characteristics with those of the two mainstream detector types: monolithic and mechanically pixelated arrays. Among countless optical barrier patterns, we explore barriers arranged in a pixel-like pattern extending all-the-way or half-way through a 20 mm thick LYSO:Ce crystal. We analyze the performance of the detectors coupled to MPPC arrays, in terms of light response functions, flood maps, line profiles, and light collection efficiency. Our results show that laser-processed detectors with both barrier patterns constitute a new detector category with a behavior between that of the two standard detector types. Results show that when the barrier-crystal interface is smooth, no DOI information can be obtained regardless of barrier refractive index (RI). However, with a rough barrier-crystal interface we can extract multiple levels of DOI. Lower barrier RI results in larger light confinement, leading to better transverse resolution. Furthermore we see that the laser-processed crystals have the potential to increase the light collection efficiency, which could lead to improved energy resolution and potentially better timing resolution due to higher signals. For a laser-processed detector with smooth barrier-crystal interfaces the light collection efficiency is simulated to  >42%, and for rough interfaces  >73%. The corresponding numbers for a monolithic crystal is 39% with polished surfaces, and 71% with rough surfaces, and for a mechanically pixelated array 35% with polished pixel surfaces and 59% with rough surfaces.

Implantable neural interfaces advance the possibilities for neuroscientists to study the brain. They are also promising for use in a multitude of bioelectronic therapies. Electrode technology plays a central role in these developments, as the electrode surfaces form the physical interfaces between technology and the biological targets. Despite this, a common understanding of how electrodes should best be evaluated and compared with respect to their efficiency in recording and stimulation is currently lacking. Without broadly accepted performance tests, it is difficult to rank the many suggestions for electrode materials available in the literature, or to identify where efforts should be focused to advance the field most efficiently. This tutorial critically discusses the most relevant performance tests for characterization of neural interface electrodes and explains their implementation, interpretation and respective limitations. We propose a unified standard to facilitate transparent reporting on electrode performance, promote efficient scientific process and ultimately accelerate translation into clinical practice.

Ionizing radiation is often used within medicine for diagnosis and treatments. Because ionizingradiation can be harmful to the body, it is important to know how it affects the tissue. Dosimetryis the study of how ionizing radiation deposits energy in a material. To measure how much ionizingradiation is deposited in the body, gas-filled detectors are often used. An ionization chamber isa type of gas-filled detector and exists in different shapes and sizes, depending on what kind ofmeasurements it is made for. Because ionization chambers are relatively expensive, it is often notpossible to buy one for each type of measurement that is to be done. This results in ionizationchambers being used for measurements they are not optimized for. This report evaluates thepossibility of 3D printing ionization chambers to make it easier to optimize them for specificmeasurements. The process included creating models of ionization chambers using CAD-software,slicing them and then 3D printing them. The 3D printed models were then brought to the SwedishRadiation Safety Authority for measurements. The ionization chambers were connected to highvoltage, and exposed to ionizing radiation in the form of high-intensity gamma-ray fields. Theoutput current of the ionization chamber was measured, which is proportional to the field intensity.The results were similar to those of a commercial ionization chamber. The conclusion is that it ispossible to 3D print ionization chambers. However, to get more accurate results, the design has tobe further optimized and more measurements need to be done.

Approximately 20% of the body’s energy consumption is ongoingly consumed by the brain, where the main part is due to the neural activity, which is only increased slightly when doing a demanding task. This ongoingly neural activity are studied with the so called resting-state fMRI, which mean that the neural activity in the brain is measured for participants with no specific task. These studies have been useful to understand the neural function and how the neural networks are constructed and cooperate. This have also been helpful in several clinical research, for example have differences been identified between bipolar disorder and major depressive disorder. Recent research has focused on temporal properties of the ongoing activity and it is well known that neural activity occurs in bursts. In this study, resting-state fMRI data and temporal graph theory is used to develop a point based method (PBM) to quantify these bursts at a nodal level. By doing this, the bursty pattern can be further investigated and the nodes showing the most bursty pattern (i.e hubs) can be identified. The method developed shows a robustness regarding several different aspects. In the method is two different variance threshold algorithms suggested. One local variance threshold (LVT) based on the individual variance of the edge time-series and one global variance threshold (GVT) based on the variance of all edges time-series, where the GVT shows the highest robustness. However, the choice of threshold needs to be adapted for the aims of the current study. Finally, this method ends up in a new measure to quantify this bursty pattern named bursty centrality. The derived temporal graph theoretical measure was correlated with traditional static graph properties used in resting state and showed a low but significant correlation. By applying this method on resting-state fMRI data for 32 young adults was it possible to identify regions of the brain that showed the most dynamic properties, these regions differed between the two thresholding algorithms

Work-related traumatic brain injuries (wrTBIs) in the construction industry have been studied in North America but, to the best of our knowledge, not in Europe. This study analyzed sets of public data on head injuries occurring in the construction industry from the workers' compensation systems in Sweden and Germany, 2014 - 2018. The ratio of wrTBI varied from 11% to 61% of all head injuries, with higher ratios for more severe injuries. The average yearly incidence (per 100,000 FTE) of wrTBI resulting in more than four days absence from work was nine in Sweden and 117 in Germany, as compared to 22-212 in North American studies. A limitation of studies based on workers' compensation claims is that they underestimate the true burden of wrTBI. The most frequent events leading to wrTBI were falls, followed by loss of control, failure of material agents, and body movements without stress. Falls from a height caused 35% of all wrTBI with more than 14 days off work in Sweden and 57% of all new injury pensions granted in Germany. In North American studies, 52-78% of the wrTBI were caused by falls. This highlights the relevance of fall safety measures to reduce wrTBI in the construction industry, such as avoiding work at heights, use of safety nets, education, and etcetera. The energy absorption of safety helmets mainly protects the head excluding face of which 49-62% were wrTBI, indicating that helmet testing standards should evaluate protection against TBI as well as skull fractures.

PURPOSE:

Veno-venous ECMO is indicated in reversible life-threatening respiratory failure without life-threatening circulatory failure. Recirculation of oxygenated blood in the ECMO circuit decreases efficiency of patient oxygen delivery but is difficult to measure. We seek to identify and quantify some of the factors responsible for recirculation in a simulation model and compare with clinical data.

METHODS:

A closed-loop real-time simulation model of the cardiovascular system has been developed. ECMO is simulated with a fixed flow pump 0 to 5 l/min with various cannulation sites - 1) right atrium to inferior vena cava, 2) inferior vena cava to right atrium, and 3) superior+inferior vena cava to right atrium. Simulations are compared to data from a retrospective cohort of 11 consecutive adult veno-venous ECMO patients in our department.

RESULTS:

Recirculation increases with increasing ECMO-flow, decreases with increasing cardiac output, and is highly dependent on choice of cannulation sites. A more peripheral drainage site decreases recirculation substantially.

CONCLUSIONS:

Simulations suggest that recirculation is a significant clinical problem in veno-venous ECMO in agreement with clinical data. Due to the difficulties in measuring recirculation and interpretation of the venous oxygen saturation in the ECMO drainage blood, flow settings and cannula positioning should rather be optimized with help of arterial oxygenation parameters. Simulation may be useful in quantification and understanding of recirculation in VV-ECMO.

PURPOSE:

Veno-venous ECMO is indicated in reversible life-threatening respiratory failure without life-threatening circulatory failure. Recirculation of oxygenated blood in the ECMO circuit decreases efficiency of patient oxygen delivery but is difficult to measure. We seek to identify and quantify some of the factors responsible for recirculation in a simulation model and compare with clinical data.

METHODS:

A closed-loop real-time simulation model of the cardiovascular system has been developed. ECMO is simulated with a fixed flow pump 0 to 5 l/min with various cannulation sites - 1) right atrium to inferior vena cava, 2) inferior vena cava to right atrium, and 3) superior+inferior vena cava to right atrium. Simulations are compared to data from a retrospective cohort of 11 consecutive adult veno-venous ECMO patients in our department.

RESULTS:

Recirculation increases with increasing ECMO-flow, decreases with increasing cardiac output, and is highly dependent on choice of cannulation sites. A more peripheral drainage site decreases recirculation substantially.

CONCLUSIONS:

Simulations suggest that recirculation is a significant clinical problem in veno-venous ECMO in agreement with clinical data. Due to the difficulties in measuring recirculation and interpretation of the venous oxygen saturation in the ECMO drainage blood, flow settings and cannula positioning should rather be optimized with help of arterial oxygenation parameters. Simulation may be useful in quantification and understanding of recirculation in VV-ECMO.

Surface Electromyography (sEMG) has applications in prosthetics, diagnostics and neuromuscular rehabilitation. Self-adhesive Ag/AgCl are the electrodes preferentially used to capture sEMG in short-term studies, however their long-term application is limited. In this study we designed and evaluated a fully integrated smart textile band with electrical connecting tracks knitted with intarsia techniques and knitted textile electrodes. Real-time myoelectric pattern recognition for motor volition and signal-to-noise ratio (SNR) were used to compare its sensing performance versus the conventional Ag-AgCl electrodes. After a comprehending measurement and performance comparison of the sEMG recordings, no significant differences were found between the textile and the Ag-AgCl electrodes in SNR and prediction accuracy obtained from pattern recognition classifiers.

Purpose: Current photon-counting computed tomography detectors are limited to a pixel size of around 0.3 to 0.5 mm due to excessive charge sharing degrading the dose efficiency and energy resolution as the pixels become smaller. In this work, we present measurements of a prototype photon-counting detector that leverages the charge sharing to reach a theoretical sub-pixel resolution in the order of 1 μm. The goal of the study is to validate our Monte-Carlo simulation using measurements, enabling further development. Approach: We measure the channel response at the MAX IV Lab, in the DanMAX beamline, with a 35 keV photon beam, and compare the measurements with a 2D Monte Carlo simulation combined with a charge transport model. Only a few channels on the prototype are connected to keep the number of wire bonds low. Results: The measurements agree generally well with the simulations with the beam close to the electrodes but diverge as the beam is moved further away. The induced charge cloud signals also seem to increase linearly as the beam is moved away from the electrodes. Conclusions: The agreement between measurements and simulations indicates that the Monte-Carlo simulation can accurately model the channel response of the detector with the photon interactions close to the electrodes, which indicates that the unconnected electrodes introduce unwanted effects that need to be further explored. With the same Monte-Carlo simulation previously indicating a resolution of around 1 μm with similar geometry, the results are promising that an ultra-high resolution detector is not far in the future.

Functional Magnetic Resonance Imaging (fMRI) is one of the best techniques for neuroimaging and has revolutionized the way to understand the brain functions. It measures the changes in the blood oxygen level-dependent (BOLD) signal which is related to the neuronal activity. Complexity of the data, presence of different types of noises and the massive amount of data makes the fMRI data analysis a challenging one. It demands efficient signal processing and statistical analysis methods.  The inference of the analysis is used by the physicians, neurologists and researchers for better understanding of the brain functions.

     The purpose of this study is to design a toolbox for fMRI data analysis. It includes methods to detect the brain activity maps, estimation of the hemodynamic response (HDR) and the connectivity of the brain structures. This toolbox provides methods for detection of activated brain regions measured with Bayesian estimator. Results are compared with the conventional methods such as t-test, ordinary least squares (OLS) and weighted least squares (WLS). Brain activation and HDR are estimated with linear adaptive model and nonlinear method based on radial basis function (RBF) neural network. Nonlinear autoregressive with exogenous inputs (NARX) neural network is developed to model the dynamics of the fMRI data.  This toolbox also provides methods to brain connectivity such as functional connectivity and effective connectivity.  These methods are examined on simulated and real fMRI datasets.

Applications based on measurements of Electrical Bioimpedance (EBI) spectroscopy analysis, like assessment of body composition, have proliferated in the past years. Currently Body Composition Assessment (BCA) based in Bioimpedance Spectroscopy (BIS) analysis relays on an accurate estimation of the Cole parameters R-0 and R-infinity. A recent study by Bogonez-Franco et al. has proposed electrode mismatch as source of remarkable artefacts in BIS measurements. Using Total Right Side BIS measurements from the aforementioned study, this work has focused on the influence of electrode mismatch on the estimation of R-0 and R-infinity using the Non-Linear Least Square curve fitting technique on the modulus of the impedance. The results show that electrode mismatch on the voltage sensing electrodes produces an overestimation of the impedance spectrum leading to a wrong estimation of the parameters R-0 and R-infinity, and consequently obtaining values around 4% larger that the values obtained from BIS without electrode mismatch. The specific key factors behind electrode mismatch or its influence on the analysis of single and spectroscopy measurements have not been investigated yet, no compensation or correction technique is available to overcome the deviation produced on the EBI measurement. Since textile-enabled EBI applications using dry textrodes, i.e. textile electrodes with dry skin-electrode interfaces and potentially large values of electrode polarization impedance are more prone to produce electrode mismatch, the lack of a correction or compensation technique might hinder the proliferation of textile-enabled EBI applications for personalized healthcare monitoring.

Substantial volume defects of the head and neck oftenrequire customized solutions to improve quality of life likefree flap transfers.Titanium and its alloys are versatile materialsproviding the feature of osteointegration. The conditionswhich facilitate the deposition of lamellar bone are underextensive research. Our project aimed to determine whethertitanium can function as a scaffold - unlike simple plates - toenhance bone regeneration for load bearing structures. Thereaction of stem cells to scaffolds with varying stiffness willbe presented.Additive manufacturing were used to produce a variety ofscaffolds to optimize titanium structures. Electric beam melting(EBM) manufacturing allowed us to optimize the elasticmodulus (Young) of the titanium to match with cadaveric 

bone from a previous project. Multidirectional mechanicaltests were performed on the various designs of titanium cellstructures (n=80). The predictability and quality of manufacturingwas assessed statistically and also with scanningelectron microscope (SEM).The results demonstrated structures matching the mechanicalproperties of bone and even anisotropy as our resultssuggest 3GPa elasticity. This allows the possibility to buildregenerating bone with predictable properties. In addition,predictable patterning - unlike etching and sandblasting - ofmicroscopic (nano) features found to be significant and nonhomogenous simple repetitive patterns provide better cellularresponse.The benefit that tissue engineering techniques offer isdecreased morbidity, relative independence from donor site,with a highly specific and customized shape. Titanium basedreconstruction constructs seems to offer an alternative futurefor bony reconstruction.

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.

The lack of digital and easily accessible information for home hemodialysis patients leads to a disinclination of using the provided manuals. This problem could potentially be solved with a user-friendly app, where all information and communication with the healthcare providers could take place. Hence, leading to them feeling better prepared to take charge of their own treatment. The purpose of this thesis is to provide a minimum viable product of a mobile application to facilitate treatment for home hemodialysis patients. The app was developed using the programming language SwiftUI. Information was collected from patients with home hemodialysis treatment through interviews conducted in their homes. 

The mobile application enables patients to access the manual and failure identification codes through a search bar. It also provides digital checklists, dialysis protocol and symptom follow-up. The healthcare providers can then access the dialysis protocol and symptom follow-up through remote patient monitoring. A chat function allows patients to contact nurses. The mobile application fulfills the clients’ requests and with additional work it could be fully operational. 

In the manufacturing industry, predictive maintenance (PdM) stands out by leveraging data analytics and IoT technologies to predict machine failures, offering a significant advancement over traditional reactive and scheduled maintenance practices. The aim of this thesis was to examine how anomaly detection algorithms could be utilized to anticipate potential breakdowns in manufacturing operations, while also investigating the feasibility and potential benefits of integrating PdM strategies into a production line. The methodology of this projectconsisted of a literature review, application of machine learning (ML) algorithms, and conducting interviews. Firstly, the literature review provided a foundational basis to explore the benefits of PdM and its impact on production line productivity, thereby shaping the development of interview questions. Secondly, ML algorithms were employed to analyze data and predict equipment failures. The algorithms used in this project were: Isolation Forest (IF), Local Outlier Factor (LOF), Logistic Regression (LR), One-Class Support Vector Machine(OC-SVM) and Random Forest (RF). Lastly, interviews with production line personnel provided qualitative insights into the current maintenance practices and perceptions of PdM. The findings from this project underscore the efficacy of the IF model in identifying potential equipment failures, emphasizing its key role in improving future PdM strategies to enhance maintenance schedules and boost operational efficiency. Insights gained from both literature and interviews underscore the transformative potential of PdM in refining maintenance strategies, enhancing operational efficiency, and minimizing unplanned downtime. More broadly, the successful implementation of these technologies is expected to revolutionize manufacturing processes, driving towards more sustainable and efficient industrial operations. 

Measuring transmembrane water exchange can provide potential biomarkers for tumors and brain disorders. Diffusion Magnetic Resonance Imaging (dMRI) is a well-established tool that can non-invasively measure water exchange across cell membranes. Diffusion Exchange Spectroscopy (DEXSY) is one of the dMRI-based frameworks used to estimate exchange. DEXSY provides a detailed picture of multi-site exchange processes but requires a large quantity of data. Several models based on the DEXSY framework have been proposed to reduce the acquisition time. Filter Exchange Imaging (FEXI) and curvature models are two of them that only require certain samples of the DEXSY dataset. Diffusion-Exchange Weighted (DEW) Imaging model is another data reduction method accounting for restricted diffusion within cells and can use a specific subset of the DEXSY dataset to measure exchange. Furthermore, a more general expression of the DEXSY signal, referred to as the general model, can theoretically analyze the full space or reduced DEXSY datasets and estimate exchange. However, the results of the subsampling schemes and the data reduction models have not been compared to the full space estimation. 

Therefore, this thesis aims to experimentally explore the feasibility of estimating exchange using these four models (the general, FEXI, curvature and DEW models) with the data acquired using a low-field benchtop MR scanner, and compare the estimates from the general model with different subsampling schemes and the data reduction models to the full space estimation. For this purpose, a double diffusion encoding (DDE) sequence was modified from an existing sequence on the benchtop MR scanner and a DEXSY experiment was conducted on this MR scanner and a yeast phantom to acquire a full space dataset. The exchange parameters estimated from the full space dataset using the general model were used as "ground truths" to evaluate the estimates from the reduced datasets analyzed using the general, FEXI and curvature models. Moreover, two alternative subsampling schemes named the shifted DEW and new trajectory schemes were proposed and employed to measure exchange.

The results indicate that all the methods except the curvature sampling scheme employed with both the general and curvature models provided comparable estimates to the "ground truths". The shifted DEW and new trajectory sampling schemes performed better over others in terms of consistency with the "ground truths" and low variations between voxels, suggesting the theoretical and experimental optimization of these two subsampling schemes can be further studied and developed.

Patients treated at intensive care units (ICUs) are failing in one or several organs and requireappropriate monitoring and treatment in order to maintain a meaningful life. Today clinicians inintensive care units (ICUs) manage a large amount of data generated from monitoring devices.The monitoring parameters can either be noted down manually on a monitoring sheet or, for some parameters, transferred automatically to storage. In both cases the information is stored withthe aim to support clinicians throughout the intensive care and be easily accessible. Patient datamanagement systems (PDMSs) facilitate ICUs to retrieve and integrate data. Before managinga new configuration of patient data system, it is required that the ICU makes careful analysis ofwhat data desired to be registered. This pilot study provides knowledge of how the monitoringis performed in an Intensive Care Unit in an emergency hospital in Stockholm.The aim of this thesis project was to collect data about what the clinicians require and whatequipment they use today for monitoring. Requirement elicitation is a technique to collectrequirements. Methods used to collect data were active observations and qualitative interviews.Patterns have been found about what the assistant nurses, nurses and physicians’ require of systems supporting the clinician’s with monitoring parameters. Assistant nurses would like tobe released from tasks of taking notes manually. They also question the need for atomized datacollection since they are present observing the patient bed-side. Nurses describe a demanding burden of care and no more activities increasing that burden of care is required. Physicians require support in order to see how an intervention leads to a certain result for individual patients.The results also show that there is information about decision support but no easy way to applythem, better than the ones used today. Clinicians state that there is a need to be able to evaluatethe clinical work with the help of monitoring parameters. The results provide knowledge about which areas the clinicians needs are not supported enough by the exciting tools.To conclude results show that depending on what profession and experience the clinicians have the demands on monitoring support di↵ers. Monitoring at the ICU is performed while observing individual patients, parameters from medical devices, results from medical tests and physical examinations. Information from all these sources is considered by the clinicians and is desired to be supported accordingly before clinicians commit to action resulting in certain treatment,diagnosis and/or care.

Prostate cancer is the most common malignancy diagnosed in western men today. Measuring the amount of prostate-specific antigen in blood is the most widespread tool for diagnosing prostate cancer early on. However, clinical investigations show that many people with high levels of PSA can be healthy and vice versa. Ultrasound-guided biopsies is the clinical method used to prove prostate cancer. Unfortunately, many tumors are overlooked by this procedure. It has been estimated that about 30% of the biopsy examinations fail to find a present tumor. The resonance technique, which can differentiate hard and soft materials, has in vitro been shown to be able to distinguish prostate cancer and healthy epithelial prostate tissue. Raman spectroscopy is also a good candidate for detecting prostate cancer. Our novel idea of combining the resonance and Raman techniques into a portable probe, appropriate for in vivo examinations of the prostate, will be discussed in this report. The two techniques are presented and explained, and the feasibility of combining them is discussed. Planned experiments and investigations are outlined.

Prostate cancer (PCa) is the most common male cancer in Europe and the US, and only lung and colorectal cancer have a higher mortality among European men. In Sweden, PCa is the most common cause of cancer-related death for men.The overall aim of this thesis was to explore the need for new and complementary methods for PCa detection and to take the rst step towards a novel approach: combining the tactile resonance method (TRM) and Raman spectroscopy (RS). First, the main methods for PCa detection were reviewed. Second, to establish a robust protocol for RS experiments in vitro, the eects of snap-freezing and laser illumination on porcine prostate tissue were studied using RS and multivariate statistics. Third, measurements on porcine and human tissue were performed to compare the TRM and RS data via multivariate techniques, and to assess the accuracy of classifying healthy and cancerous tissue using a support vector machine algorithm.It was concluded through the literature review that the gold standard for PCa detection and diagnosis, the prostate specic antigen test and systematic biopsy, have low sensitivity and specicity. Indolent and aggressive tumors cannot be reliably dierentiated, and many men are therefore treated either unnecessarily or too late. Clinical benets of the state-of-the-art in PCa imaging - advanced ultrasound and MR techniques - have still not been convincingly shown. There is a need for complementary and cost-eective detection methods. TRM and RS are promising techniques, but hitherto their potential for PCa detection have only been investigated in vitro.In the RS study no evidence of tissue degradation due to 830 nm laser illumination at an irradiance of ∼3 · 1010 W m-2 were found. Snap-freezing and subsequent storage at -80° C gave rise to subtle but signicant changes in Raman spectra, most likely related to alterations in the protein structure. The major changes due to PCa do not seem to be related to the protein structure, hence snap-freezing may be applied in our experiments.The combined measurements on porcine and human prostate tissue showed that RS provided additional discriminatory power to TRM. The classication accuracy for healthy porcine prostate tissue, and for healthy and cancerous human prostate tissue, was > 73%. This shows the power of the support vector machine applied to the combined data.In summary, this work indicates that an instrument combining TRM and RS is a promising complementary method for PCa detection. Snap-freezing of samples may be used in future RS studies of PCa. A combined instrument could be used for tumor-border demarcation during surgery, and potentially for guiding prostate biopsies towards lesions suspicious for cancer. All of this should provide a more secure diagnosis and consequently more effcient treatment of the patient.

Prostate cancer (PCa) is the most common male cancer in Europe and the US, and only lung and colorectal cancer have a higher mortality among European men. In Sweden, PCa is the most common cause of cancer-related death for men.The overall aim of this licentiate work was to explore the need for new and complementary methods for PCa detection and to take the first step towards a novel approach: combining Raman spectroscopy and resonance sensor technology. Firstly, the main methods for PCa detection were reviewed. Secondly, to establish a robust protocol for Raman experiments in vitro, the effects of snap-freezing and laser illumination on porcine prostate tissue were studied using Raman spectroscopy and multivariate statistics. Thirdly, measurements on pork belly tissue using both a resonance sensor and a Raman fiberoptic probe were evaluated regarding correlation of the data.It was concluded that the gold standard for PCa detection and diagnosis, the prostate specific antigen test and systematic biopsy, have low sensitivity and specificity. Indolent and aggressive tumors cannot be reliably differentiated, and many men are therefore treated either unnecessarily or too late. Clinical benefits of the state-of-the-art in PCa imaging - advanced ultrasound and MR techniques - have still not been convincingly shown. There is a need for complementary and cost-effective detection methods. Raman spectroscopy and resonance sensor technology are promising alternative techniques, but hitherto their potential for PCa detection have only been investigated in vitro.No evidence of tissue degradation due to 830 nm laser illumination at an irradiance of 3 1010 W/m2 were found. Snap-freezing and subsequent storage at -80◦C gave rise to subtle but significant changes in Raman spectra, most likely related to alterations in the protein structure. The major changes in cancerous prostate tissue do not seem to be related to the protein structure, hence snap-freezing may be applied.The combined measurements on pork belly tissue showed that Raman spectroscopy provided additional discriminatory power to the resonance sensor. The Raman data explained 67% of the variability of the stiffness parameter. The differentiation of tissue types using the resonance sensor was relatively poor, likely due to its large sample volume compared to the Raman sensor. A smaller resonance sensor tip may improve the results.In summary, this work indicates that an instrument combining Raman spectroscopy and resonance sensor technology is a promising complementary method for PCa detection. Snap-freezing of samples may be used in future Raman studies of PCa. A combined instrument could potentially be used to guide prostate biopsies towards lesions suspicious for cancer, and for tumor-border demarcation during surgery. All of this should provide a more secure diagnosis and consequently more efficient treatment of the patient.

The tactile resonance method (TRM) and Raman spectroscopy (RS) are promising for tissue characterisation in vivo. Our goal is to combine these techniques into one instrument, to use TRM for swift scanning, and RS for increasing the diagnostic power. The aim of this study was to determine the classification accuracy, using support vector machines, for measurements on porcine tissue and also produce preliminary data on human prostate tissue. This was done by developing a new experimental setup combining micro-scale TRM — scanning haptic microscopy (SHM) — for assessing stiffness on a micro-scale, with fibre optic RS measurements for assessing biochemical content. We compared the accuracy for using SHM alone versus SHM combined with RS, for different degrees of tissue homogeneity. The cross-validation classification accuracy for healthy porcine tissue types using SHM alone was 65–81%, and when RS was added it was increased to 81–87%. The accuracy for healthy and cancerous human tissue was 67–70% when only SHM was used, and increased to 72–77% for the combined measurements. This shows that the potential for swift and accurate classification of healthy and cancerous prostate tissue is high. This is promising for developing a tool for probing the surgical margins during prostate cancer surgery.

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard-–histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.