Turbulent blood flow is involved in the pathogenesis of several cardiovascular diseases. While it is known that turbulence is present in patients with obstructive disease in the major vessels, the magnitude and impact of turbulence in the normal heart and aorta is still relatively unexplored. Besides, existing analysis method of the blood flow is a labour intensive process and requires excessive amount of time. A method to automatically create hemodynamic atlases has been developed, using 4D Flow magnetic resonance imaging (MRI), a powerful tool to measure blood flow characteristics. The resulting atlases show the expected blood flow characteristics in the aorta for a group of similar subjects. Application of the method in healthy young and healthy old has shown significant differences in kinetic energy and turbulent kinetic energy in the aortic flow.

This thesis investigates if support vector machine classification is a suitable approach when performing automatic segmentation of knee cartilage using quantitative magnetic resonance imaging data. The data sets used are part of a clinical project that investigates if patients that have suffered recent knee damage will develop cartilage damage. Therefore the thesis also investigates if the segmentation results can be used to predict the clinical outcome of the patients. Two methods that perform the segmentation using support vector machine classification are implemented and evaluated. The evaluation indicates that it is a good approach for the task, but the implemented methods needs to be further improved and tested on more data sets before clinical use. It was not possible to relate the cartilage properties to clinical outcome using the segmentation results. However, the investigation demonstrated good promise of how the segmentation results, if they are improved, can be used in combination with quantitative magnetic resonance imaging data to analyze how the cartilage properties change over time or vary between knees.

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.

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An initiative to standardize the nomenclature in Sweden started in 2016 along with the creation of the local database Medical Information Quality Archive (MIQA) and a national radiotherapy register on Information Network for CAncercare (INCA). A problem of identifying the clinical tumor volume (CTV) structures and prescribed dose arose when the consecutive number, which is added to the CTV-name, was made inconsistently in MIQA and INCA. Deep neural networks (DNN) were promising tools to solve the multi-label classification task of the CTV to enable automatic labeling in the database. Prostate cancer patients that often have more than one type of organ in the same CTV structure were chosen for proof of concept. The DNN used supervised training in a 2D fashion where the radiation therapy (RT) structures along with the CT image were fed, slice by slice, to AlexNet and VGGNet to label the CTV structures in the local database system MIQA and INCA. The study also includes three methods to classify a final label for the CTV structure since the model makes the predictions on each slice. The three methods were maximum method by taking the maximum prediction for each class, minimum method by taking the minimum prediction for each class and occurrence method. The occurrence method chooses the maximum prediction if the network has predicted the class over 0.5 at least two times and the minimum prediction if not. The DNN and volume classification methods performed well where the maximum and occurrence method performed the best and can be used to interpret RT volumes in MIQA and INCA for prostate cancer patients. This novel study gives promising results for the future development of deep neural networks classifying RT structures for more than one type of cancer patient. / Ett initiativ för att standardisera nomenklaturen i Sverige startade 2016 tillsammans med skapandet av den lokala databasen Medical Information Quality Archive (MIQA) och ett nationellt radioterapikvalitetsregister på plattformen Information Network for CAncercare (INCA). Ett problem med att identifiera kliniska tumörvolymstrukturer (CTV-strukturer) och ordinerad dos uppstod när de på varandra följande siffrorna, som adderas till CTV-namnet för att skilja de olika CTV:erna från varandra, gjordes inkonsekvent i MIQA och INCA. Djupa neurala nätverk (DNN) är lovande verktyg för att lösa klassificeringen av CTV för att möjliggöra automatisk annotering för multippla etiketter i databasen. Prostatacancerpatienter vars radioterapistrukturer (RT-strukturer) ofta innehåller fler än ett organ användes därför för att bevisa konceptet för fleretikettsklassificering. DNN:et använde övervakad inlärning av 2D-bilder där RT-strukturerna tillsammans med CT-bilderna matades in, snitt för snitt, till AlexNet och VGGNet för att namnge CTV-strukturerna i det lokala databassystemet MIQA och sedan i INCA. Studien inkluderar även tre metoder för en slutlig strukturetikett eftersom modellen gör sina förutsägelser på varje snitt. Metoderna var maximum där den högsta förutsägelsen noteras för varje klass, minimum där den lägsta förutsägelsen noteras för varje klass och förekomst där den högsta förutsägelsen noteras om klassen har fått minst två förutsägelser över 0.5 annars noteras den lägsta förutsägelsen. DNN:en och volymetikettmetoderna gav bra resultat där maximum- och förekomstmetoden gav bäst resultat och kan användas för att tolka RT-volymer i MIQA och INCA för prostatacancerpatienter. Denna nya studie ger lovande resultat för framtida utveckling av djupa neurala nätverk som klassificerar strukturer från mer än en typ av cancerpatient.