The purpose is trying to classify the Liver Fibrosis stage using Gadoxetic Acid-EnhancedMR Images.  In the very beginning, a method proposed by one Korean group is being examined and trying to reproduce their result. However, the performance is not as impressive as theirs. Then, some gray-scale image feature extraction methods are used. Last but not least, the hottest method in recent years - ConvolutionNeural Network(CNN) was utilized. Finally, the performance has been evaluated in both methods. The result shows that with manual feature extraction, the Adaboost model works pretty well that AUC achieves 0.9. Besides, the AUC of ResNet-18 network - a deep learning architecture, can reach 0.93. Also, all the hyperparameters and training settings used on ResNet-18 can be transferred to ResNet-50/ResNet-101/InceptionV3 very well. The best model that can be obtained is ResNet-101which has an AUC of 0.96 - higher than all current publications for machine learning methods for staging liver fibrosis.

DIRA-3D is an iterative model-based reconstruction method for dual-energy helical CT whose goal is to determine the material composition of the patient from accurate linear attenuation coefficients (LACs). Possible applications are, for example, to aid in calculations of radiation transport and dose calculations in brachytherapy with low energy photons, and in proton therapy. There was a need to replace the current image reconstruction method, the PI-method, with a weighted filtered backprojection (wFBP) algorithm for image reconstruction, since wFBP is used for image reconstruction in Siemens's CT-scanners. The new DIRA-3D algorithm implemented the program take for cone-beam projection generation and the FreeCT wFBP algorithm for image reconstruction. Experiments showed that the accuracies of the resulting LACs for the DIRA-3D algorithm using wFBP for image reconstruction were comparable to the one using the PI-method for image reconstruction. The relative LAC errors reached a value below 0.2% after 10 iterations.

Each year million of babies are born pre-term, some of these pre-term births occur due to the motherhaving a too soft cervix which can not withstand the forces the baby exposes it to. The aim of thisstudy was to implement and evaluate a programmable shear wave elastography ultrasound system forcervical applications and investigate the optimal settings of shear wave elastography push voltage andshear wave elastography push focus depth. Shear wave elastography is an ultrasound based imagingmodality aiming to evaluate the tissue elasticity by using acoustic radiation forces to induce shear waves.The propagation of the shear waves through the tissue is then tracked in order to calculate the shearwave velocity which is related to the tissue elasticity. B-mode imaging, pushing sequence and planewave imaging have been implemented and measurements have been conducted on four cervical polyvinylalcohol phantoms. The acquired data has been post-processed using Loupas 2D-autocorrector to gainthe axial displacement and enabling tracking of the shear waves to allow evaluation and optimizationof the implemented method. The implemented shear wave technique showed to be able to distinguishcervical phantoms of dierent elasticity and a high pushing voltage and shallow focus push depth havebeen found to produce the most reliable results.

Radiologists often have to look through many different patients and examinations in quick succession, and to aid in the workflow the different types of images should be presented for the radiologist in the same manner and order between each new examination. Thus decreasing the time needed for the radiologist to either find the correct image or rearrange the images to their liking. A step in thisprocess requires a comparison between two images to be made and produce a score between 0-1 describing how similar the images are. A similar algorithm already exists at Sectra, but that algorithm only uses the metadata from the images without considering the actual pixel data. The aim of this thesis were to explore different methods of doing the same comparison as the previous algorithm but only using the pixel data. Considering only 3D volumes from CT examinations of the abdomen and thorax region, this thesis explores the possibility of using SSIM, SIFT and SIFT together with a histogram comparison using the Bhattacharyya distance for this task. It was deemed very important that the ranking produced when ordering the images in terms of similarity to one reference image followed a specific order. This order was determined by consulting personnel at Sectra that works closely with the clinical side of radiology. SSIM were able to differentiate between different plane orientations since they usually had large resolution differences in each led, but it could not be made tofollow the desired ranking and was thus disregarded as a reliable option for this problem. The method using SIFT followed the desired ranking better, but struggled a lot with differentiating between the different contrast phases. A histogram component were also added to this method, which increased the accuracy and improved the ranking. Although, further development is still needed for thismethod to be a reliable option that could be used in a clinical setting.

Magnetic resonance imagining is a non-invasive 3D imaging technology widely used in the medical field for partial and full body scans. AMRA Medical AB is a medical company which combines MRI images with additional patient attributes such as height, weight, sex and age to perform analysis such as body composition profiling. However, the additional information required is not always available or accurate. Manual measurements are proneto human error, and retrieving them from patient journals is complicated due to the sensitivenature of the information. This thesis investigates technologies to instead estimate height, weight, sex and age automatically from only the MR images by the use of deep learning. If successful, such methods could eliminate the reliance on additional subject information. Alternatively they could serve as an error detection mechanism to flag possibleinaccuracies in the data, which could be of use for both AMRA as well as for operators in a clinical scenario.

A reliable evaluation system is essential for every automatic process. While techniques for automatic segmentation of images have been extensively researched in recent years, evaluation of the same has not received an equal amount of attention. Amra Medical AB has developed a system for automatic segmentation of magnetic resonance (MR) images of human bodies using an atlas-based approach. Through their software, Amra is able to derive body composition measurements, such as muscle and fat volumes, from the segmented MR images. As of now, the automatic segmentations are quality controlled by clinical experts to ensure their correctness. This thesis investigates the possibilities to leverage predictive modelling to reduce the need for a manual quality control (QC) step in an otherwise automatic process. Two different regression approaches have been implemented as a part of this study: body composition measurement prediction (BCMP) and manual correction prediction (MCP). BCMP aims at predicting the derived body composition measurements and comparing the predictions to actual measurements. The theory is that large deviations between the predictions and the measurements signify an erroneously segmented sample. MCP instead tries to directly predict the amount of manual correction needed for each sample. Several regression models have been implemented and evaluated for the two approaches. Comparison of the regression models shows that local linear regression (LLR) is the most performant model for both BCMP and MCP. The results show that the inaccuracies in the BCMP-models, in practice, renders this approach useless. MCP proved to be a far more viable approach; using MCP together with LLR achieves a high true positive rate with a reasonably low false positive rate for several body composition measurements. These results suggest that the type of system developed in this thesis has the potential to reduce the need for manual inspections of the automatic segmentation masks.

By generating a synthetic CT image (sCT) directly from the MRI, the electron density can be calculated, and the CT examination can be excluded from the patient flow minimizing the risk of uncertainties in the registration. Basing the radiation treatment process solely on MR images is called MRI-only and is beneficial for the patient as it can provide more accurate radiation treatment than the standardised treatment with fewer CT examinations and possibly a more cost-effective radiation treatment process.  The conventional coils that are normally used in MRI for dose planning purpose cannot be placed directly on the patient as the outer body contour then can be deformed by these relatively heavy coils. The coils are therefore placed on a special holder which creates distance between the coil and the patient, this degrades the signal to noise ratio (SNR). The department for radiation treatment at Linköping University Hospital has access to a newly developed coil with so-called Air Technology. This type of coil is significantly lighter than the conventional ones and the idea is that this coil can be placed directly on the patient without causing deformation.  The aim of this project is to develop a software tool to validate an MRI-only workflow and to investigate if the radiation dose calculation based on sCT data differs from calculations based on CT data. Furthermore, to examine if the AIR coil has any effect on the body contour and the calculated dose.  For the evaluation of the AIR coil three similarity comparison methods were used, Hausdorff distance, Dice similarity coefficient (DSC) and Surface DSC. The result for the Hausdorff distance showed that eight out of eleven comparisons were within 4 mm difference, this corresponded good with Surface DSC where eight out of eleven had a result over 99% at a 3 mm tolerance. DSC measures gave above 98.5% for nine out of eleven of the comparisons.  The investigation on whether the radiation dose calculation differed was done using the dose- volume histogram statistics in Eclipse. A method calculating the gamma index was implemented in MICE. The results showed that nine out of ten gamma indexes had deviations that were within the same range. An explanation for why the results of one patient were not within the same range as the others could not be found and needs further investigations.

Background. Many video games allow for creative expression. Attractive Interactive AB is developing such a game, in which players draw their own levels using pen and paper. For such a game to work, collision geometry needs to be generated from photos of hand-drawn video game levels. Objectives. The main goal of the thesis is to create an algorithm for generating collision geometry from photos of hand-drawn video game levels and to determine whether the generated geometry can replace handcrafted geometry. Handcrafted geometry is manually created using vector graphics editing tools. Methods. A method for generating collision geometry from photos of drawings is implemented. The quality of the generated geometry is evaluated and compared to handcrafted geometry in terms of vertex count and positional accuracy. Ground truths are used to determine the positional accuracy of collision geometry by calculating the resemblance of the created collision geometry and the respective ground truth. Results. The generated geometry has a higher positional accuracy and on average a lower vertex count than the handcrafted geometry. Performance measurements for two different configurations of the algorithm are presented. Conclusions. Collision geometry generated by the presented algorithm has a higher quality than handcrafted geometry. Thus, the generated geometry can replace handcrafted geometry. / Bakgrund. Många datorspel möjliggör kreativa yttringar. Attractive Interactive AB utvecklar ett sådant spel, i vilket spelare ritar sina egna nivåer med hjälp av papper och penna. För att ett sådant spel ska vara möjligt måste kollisionsgeometri genereras från foton av handritade spelnivåer. Syfte. Syftet med examensarbetet är att skapa en algoritm för att generera kollisionsgeometri från foton av handritade datorspelsnivåer och fastställa om den genererade geometrin kan ersätta handgjord geometri. Handgjord kollisionsgeometri skapas manuellt genom använding av redigeringsverktyg för vektorgrafik. Metod. En metod för att generera kollisionsgeometri från foton av ritade datorspelsnivåer implementeras. Kvaliteten av den genererade geometrin evalueras och jämförs med handgjord geometri i fråga om vertexantal och positionsnoggrannhet. Grundreferenser används för att fastställa positionsnoggrannheten av kollisionsgeometri genom att beräkna likheten av den skapta kollisionsgeometrin och respektive grundreferens. Resultat. Den genererade geometrin har en högre positionsnoggrannhet och i genomsnitt ett lägre vertexantal än den handgjorda geometrin. Prestandamätningar för två olika konfigurationer av algoritmen presenteras. Slutsatser. Kollisionsgeometrin som genererats av den föreslagna algoritmen har en högre kvalitet än handgjord geometri. Därmed kan den genererade geometrin ersätta handgjord geometri.

Syftet med studien har varit att undersöka hur artificiell intelligens (AI) påverkar sjukvården. Forskning inom bildbehandling har visat på stor framgång och idag körs det en AI pilot på Danderyds Sjukhus. Studien har fokuserat på hur olika yrkesroller som forskare, sjukvårdspersonal, politiker och systemleverantörer upplever att AI kan förändra arbetssätt inom radiologi. Undersökningens datainsamling sker genom semi-strukturerade intervjuer och sekundärdata för att kunna besvara hur användningen av AI kan påverka sjukvårdens organisation.Resultatet visar på att det finns en resursbrist i sjukvården där det finns problematik med att hinna utföra samtliga arbetsuppgifter och sjukvården ser att antalet patienter kommer öka. AI visar på att ha goda möjligheter till att effektivisera arbetsflödet och möjligheten att utföra bättre diagnoser via olika verktyg. Det finns även tekniska hinder som omfattar datatillgångar, datavalidering och implementering av nya system till existerande system. Tillslut finns det även legala faktorer som skapar utmaningar.Slutsatserna från studien har beskrivit hur arbetssätt kommer att förändras och hur denna process går till samt att det har identifierats en ny roll i sjukvården. Studien har framställt att det finns ett stort gap mellan den teknologiska utvecklingen och implementation i klinisk praxis. Studien föreslår att man borde fokusera på att minska detta gap genom att bland annat sätta upp regleringar för AI. / The purpose of the study was to examine the impact of artificial intelligence (AI) on healthcare. Research in imaging has shown great success and today there is an AI pilot at Danderyds Hospital. The study has focused on different professional roles such as researchers, healthcare professionals, politicians and system vendors and their perspective on how AI will change the work methods within radiology. The survey data collection is conducted through semi-structured interviews and secondary data to answer how the use of AI will affect the healthcare organization. The result shows that there is a shortage of resources in the healthcare sector, where there are problems with performing all duties and furthermore the healthcare is seeing that the number of patients are increasing. AI shows good opportunities to streamline workflow and the ability to perform better diagnoses through different tools. There are also technical barriers that include data assets, data validation and implementing new systems to existing systems. Finally, there are legal factors that create challenges. The conclusions from the study have described how the working methods will change and how this process will work and that a new role in healthcare has been identified. The study has suggested that there is a large gap between technical development and implementation in clinical practice. The study proposes focusing on reducing this gap by setting up regulations for AI.

Bakgrund: Magnetresonanstomografi (MR) är associerat med olika risker relaterade till magnetfälten. Enligt patientsäkerhetslagen ska vårdpersonalen arbeta för patientens säkerhet och undvika vårdskador. Om vårdpersonal inte känner till risker med de olika magnetfälten kan detta leda till en brist i MR-säkerheten. Syfte: Att beskriva erfarenheter och upplevelser av MR-undersökningar och MR-säkerhet hos vårdpersonal som följer med patienter vid en MR-undersökning. Metod: En kvalitativ metod användes för denna studie med semi-strukturerade intervjuer som datainsamlingsmetod. Totalt deltog16 informanter från ett universitetssjukhus i Sverige i studien. Insamlade data analyserades med manifest innehållsanalys. Resultat: Resultatet visar att informanterna upplever att MR-undersökningar kräver mycket förberedelser för vårdpersonalen och innebär ofta lång undersökningstid. Informanternas erfarenhet av att medfölja till MR-undersökningar var generellt liten på grund av att det ofta var långt mellan undersökningstillfällena och att detta kan leda till glömska av vad som är tillåtet och inte tillåtet vid MR-kameran som medföljande vårdpersonal. Det uppkom av informanternas beskrivningar att metall utgör risker nära magnetkameran och att det finns risker för brännskador för patienter. Resultatet visade även att informanter litade på sig själva gällande MR-säkerhet men att det även fanns en önskan om att få ta del av mer utbildning i ämnet. Slutsats: Vårdpersonalens erfarenheter och upplevelser om MR-underökningar och MR- säkerhet visar att kunskap om risker med MR-undersökningar förekommer och att mycket förberedelser krävs. Erfarenheten av MR-undersökningar bland informanterna var liten och likaså kunskapen om MR-säkerhetsutbildning.