There are many clinical situations where magnetic resonance imaging (MRI) is preferable over other imaging modalities, while the major disadvantage is the relatively long scan time. Due to limited resources, this means that not all patients can be offered an MRI scan, even though it could provide crucial information. It can even be deemed unsafe for a critically ill patient to undergo the examination. In MRI, there is a trade-off between resolution, signal-to-noise ratio (SNR) and the time spent gathering data. When time is of utmost importance, we seek other methods to increase the resolution while preserving SNR and imaging time. In this work, I have studied one of the most promising methods for this task. Namely, constructing super-resolution algorithms to learn the mapping from a low resolution image to a high resolution image using convolutional neural networks. More specifically, I constructed networks capable of transferring high frequency (HF) content, responsible for details in an image, from one kind of image to another. In this context, contrast or weight is used to describe what kind of image we look at. This work only explores the possibility of transferring HF content from T1-weighted images, which can be obtained quite quickly, to T2-weighted images, which would take much longer for similar quality. By doing so, the hope is to contribute to increased efficacy of MRI, and reduce the problems associated with the long scan times. At first, a relatively simple network was implemented to show that transferring HF content between contrasts is possible, as a proof of concept. Next, a much more complex network was proposed, to successfully increase the resolution of MR images better than the commonly used bicubic interpolation method. This is a conclusion drawn from a test where 12 participants were asked to rate the two methods (p=0.0016) Both visual comparisons and quality measures, such as PSNR and SSIM, indicate that the proposed network outperforms a similar network that only utilizes images of one contrast. This suggests that HF content was successfully transferred between images of different contrasts, which improves the reconstruction process. Thus, it could be argued that the proposed multi-contrast model could decrease scan time even further than what its single-contrast counterpart would. Hence, this way of performing multi-contrast super-resolution has the potential to increase the efficacy of MRI.

The treatment plan for radiation therapy with protons is based on images from a computed tomography (CT) scanner. This is problematic since the photons in the x-ray beam from the CT scanner and the protons are affected differently by the tissue in the patient, which introduce an uncertainty in the track length of the protons. The hypothesis of this study is that a new generation of CT scanners (DECT), with the capacity to simultaneously scan the patient with two photon spectra of different mean energy, will improve the tissue characterisation and which in turn reduce the uncertainty in the track length of the protons. In this study, the accuracy and precision of a DECT-based method from the literature is compared to the conventional calibration method used today at the University clinics in Sweden to relate the attenuation of the photon beam to the slowing down of the protons. The methods are tested on CT images of a phantom, a plastic body containing tissue equivalent plastic inserts of known elemental composition. The results turned out to be inconclusive as there were large uncertainties in the measurements. The method has potential, as has been shown in the literature, but there are many questions that need to be answered before the method is ready to be implemented at the clinic. / En proton som färdas genom människokroppen deponerar endast en liten del av sin energi längs vägen innan den plötsligt deponerar allt i slutet på dess bana. Hur lång dess bana är beror på protonens ursprungliga energi och den atomära sammansättningen hos vävnaden den passerar igenom. Om sammansättningen är känd går det genom att justera den initiala energin bestämma banlängden. Denna egenskap gör protonen väldigt attraktiv för strålterpi, då det innbär möjligheten att behandla med hög precision samt bespara frisk vävnad onödig dos. Strålterapi med protoner planeras idag med bilder från en skiktröntgen (CT) som underlag. Ett problem med det är att röntgenstrålarna från CT-skannern påverkas annorlunda än protonerna av vävnaden, vilket introducerar en osäkerhet i protonernas banlängd. Hypotesen i denna studie är att en ny generation av CT-scanner (DECT), med möjlighet att simultant skanna patienten med två fotonspektran av olika medelenergi, på ett bättre sätt ska kunna bestämma den atomära sammansättningen för vävnaden och därmed reducera osäkerheten i protonernas banlängd. Noggrannhet och precision för en DECT-baserad metod från litteraturen jämförs med den SECT-baserade kalibreringsmetoden, som idag används på Universitetssjukhusen i Sverige för att relatera fotonstrålens dämpning i vävnaden till protonernas inbromsning. Metoderna testas på CT bilder av ett fantom, en plastkropp innehållandes olika cylindrar av vävnadsekvivalent plast med känd atomär sammansättning. Resultatet av den här studien är inte starkt nog för att bevisa hypotesen för studien. Det insamlade bildmaterialet innehåller höga brusnivåer jämfört med de som rapporteras i literaturen. Brusnivåer är så höga att det mesta av resultatet inte kan anses som statistiskt signifikant. Det är dessutom svårt att göra en direkt jämförelse av prestanda med befintlig teori för vävnadskaraktärisering, då bildmaterialet från de CT skanners som jämfördes är av olika typer. De resultat som publicerats i litteraturen visar att den DECT-baserade metoden har potential, men den här studien gör tydligt att det fortfarande finns frågor som måste besvaras innan metoden är redo att implementeras kliniskt.

The purpose of this project was to explore the performance of methods for estimating stopping power ratio (SPR) from Hounsfield units (HU) using dual energy CT scans, rather than the standard single energy CT scans, with the aim of finding a method which could outperform the current single energy stoichiometric method. Such a method could reduce the margin currently added to the target volume during treatment which is defined as 3.5 % of the range to the target volume + 1 mm . Three such methods, by Taasti, Zhu, and, Lalonde and Bouchard, were chosen and implemented in MATLAB. A phantom containing 10 tissue-like inserts was scanned and used as a basis for the SPR estimation. To investigate the variation of the SPR from day-to-day the phantom was scanned once a day for 12 days. The resulting SPR of all methods, including the stoichiometric method, were compared with theoretical SPR values which were calculated using known elemental weight fractions of the inserts and mean excitation energies from the National Institute of Standards and Technology (NIST). It was found that the best performing method was the Taasti method which had, at best, an average percentage difference from the theoretical values of only 2.5 %. The Zhu method had, at best, 4.8 % and Lalonde-Bouchard 15.6% including bone tissue or 6.3 % excluding bone. The best average percentage difference of the stoichiometric method was 3.1 %. As the Taasti method was the best performing method and shows much promise, future work should focus on further improving its performance by testing more scanning protocols and kernels to find the ones yielding the best performance. This should then be supplemented with testing different pairs of energies for the dual energy scans. The fact that the Zhu and Lalonde-Bouchard method performed poorly could indicate problems with the implementation of those methods in this project. Investigating and solving those problems is also an important goal for future projects. Lastly the Lalonde-Bouchard method should be tested with more than two energy spectra.

This thesis explores and explains a method using discrete curvature as a feature to find regions of vertices that can be classified as being likely to indicate the presence of an underlying tumor on a kidney surface mesh. Vertices are tagged based on curvature type and mathematical morphology is used to form regions on the mesh. The size and location of the tumor is approximated by fitting a sphere to this region. The method is intended to be employed in noninvasive radiotherapy with a dynamic soft tissue model. It could also provide an alternative to volumetric methods used to segment tumors. A validation is made using the images from which the kidney mesh was constructed, the tumor is visible as a comparison to the method result. The dynamic kidney model is validated using the Hausdorff distance and it is explained how this can be computed in an effective way using bounding volume hierarchies. Both the tumor finding method and the dynamic model show promising results since they lie within the limit used by practitioners during therapy.

Trauma care is the treatment of patients with injuries caused by external forces, for instance car crashes, assaults or fall accidents. These urgent patients typically arrive at the hospital’s Emergency Department, where they are treated by an interdisciplinary team of physicians and nurses, who collaborate to identify and address life-threatening injuries. In this thesis, the urgent phase of trauma care has been explored through observations of trauma calls and interviews with trauma care professionals, with the purpose of mapping the workflow and providing a basis for a discussion of IT systems within trauma radiology. The professionals, procedures and tools involved are collectively described as the initial trauma care system. There has been a focus on interaction between the units of this system, as well as on how decisions regarding treatment are made, often with the help of medical imaging. The initial trauma care system functions under significant time pressure, striving towards the well-defined objective of saving the life of the patient. To a great extent the system relies on standardized procedures, aiming for screening life-threatening injuries. The trauma team features a clear hierarchy and distinct roles, where the team leader role is considered vital for the team’s performance. Experience is valued and important for everyone, especially since the team often makes decisions, that may affect the future of the patient, based on incomplete information about the situation. Therefore, CT (computed tomography) images offer valuable decision-making support. The respondents are fairly satisfied with the current tools for viewing and manipulating radiological images. Little support for the need of improved or novel IT systems in trauma radiology is found, as is the use for 3D visualization of radiological images in this domain. Informants recognize communication failures and lacking teamwork as the major problems in trauma care. Difficulties like this may be decreased by education and training regarding these issues.

Dental radiography is one of the most frequent types of diagnostic radiological investigations performed. The equipment and techniques used are constantly evolving. However, dental healthcare has long been an area neglected by radiation safety legislation and the medical physicist community, and thus, the quality assurance (QA) regime needs an update. This project aimed to implement and evaluate objective tests of key image quality parameters for intra-oral (IO) X-ray images. The image quality parameters assessed were sensitivity, noise, uniformity, low-contrast resolution, and spatial resolution. These parameters were evaluated for repeatability at typical tube current, voltage, and exposure time settings by computing the coefficient of variation (CV) of the mean value of each parameter from multiple images. A further aim was to develop a semi-quantitative test for the correct alignment of the position indicating device (PID) with the primary collimator. The overall purpose of this thesis was to look at ways to improve the QA of IO X-rays systems by digitizing and automating part of the process. A single image receptor and an X-ray tube were used in this study. Incident doses at the receptor were measured using a radiation meter. The relationship between incident dose at the receptor and the output signal was used to determine the signal transfer curve for the receptor. The principal sources of noise in the practical exposure range of the system were investigated using a separation of noise sources based upon variance. The transfer curve of the receptor was found to be linear. Noise separation showed that quantum noise was the dominant noise. Repeatability of the image quality parameters assessed was found to be acceptable. The CV for sensitivity was less than 3%, while that for noise was less than 1%. For the uniformity measured at the center, the CV was less than 10%, while the CV was less than 5% for the uniformity measured at the edge. The low-contrast resolution varied the most at all exposure settings investigated with CV between 6 - 13%. Finally, the CV for the spatial resolution parameters was less than 5%. The method described to test for the correct alignment of the PID with the primary collimator was found to be practical and easy to interpret manually. The tests described here were implemented for a specific sensor and X-ray tube combination, but the methods could easily be adapted for different systems by simply adjusting certain parameters.

Osteoarthritis is a large problem affecting many patients globally, and diagnosis of osteoarthritis is often done using evidence from arthroscopic surgeries. Making a correct diagnosis is hard, and takes years of experience and training on thousands of images. Therefore, developing an automatic solution to perform the diagnosis would be extremely helpful to the medical field. Since machine learning has been proven to be useful and effective at classifying and segmenting medical images, this thesis aimed at solving the problem using machine learning methods. Multifractal analysis has also been used extensively for medical imaging segmentation. This study proposes two methods of automatic segmentation using neural networks and multifractal analysis. The thesis was performed using real arthroscopic images from surgeries. MultiResUnet architecture is shown to be well suited for pixel perfect segmentation. Classification of multifractal features using neural networks is also shown to perform well when compared to related studies.

This report contains a summation of a variety of articles that have been read and analysed. Each article describes different methods that can be used to detect lesions, optic disks, drusen and exudates in retinal images. I.e. diagnose e.g. Diabetic Retinopathy and Age-Related Macular Degeneration. A general approach is presented, which all methods more or less is based on. Methods to locate the optic disk The PCA  kNN Regression Hough Transform Fuzzy Convergence Vessel Direction Matched Filter Etc. The best method based on result, reliability, number of images and publisher is kNN regression. The result of this method is remarkably good and that brings some doubt about its reliability. Though the method was published at IEEE and that gives the method a more trustful look. A next best method which also is very useful is Vessel Direction Matched Filter. Methods to detect drusen – diagnose Age-Related Macular Degeneration PNN classifier Histogram approach Etc. The best method based on result, reliability, number of images and publisher is the PNN classifier. The method had a sensitivity of 94 % and a specificity of 95 %. 300 images were used in the experiment which was published by the IEEE in 2011. Methods to detect exudates – diagnose Diabetic Retinopathy Morphological techniques Luv colour space, Wiener filter an Canny edge detector. The best method based on result, reliability, number of images and publisher is an experiment called “Feature Extraction”. The method includes the Luv colour space, Wiener filter (remove noise) and the Canny edge detector. / Den här rapporten innehåller en sammanfattning av ett flertal artiklar som har blivit studerade. Varje artikel har beskrivit en metod som kan användas för att upptäcka sjuka förändringar i ögonbottenbilder, det vill säga, åldersförändringar i gula fläcken och diabetisk retinopati. Metoder för att lokalisera blinda fläcken PCA kNN regression Hough omvandling Suddig konvergens Filtrering beroende på kärlens riktning Mm. Den bästa metoden baserat på resultat, pålitlighet, antal bilder och utgivare är kNN regression. De förvånansvärt goda resultaten kan inbringa lite tvivel på huruvida resultaten stämmer. Artikeln publicerades dock av IEEE och det gör artikeln mer trovärdig. Den näst bästa metoden är filtrering beroende på kärlens riktning. Metoder för att diagnosticera åldersförändringar i gula fläcken PNN klassificeraren Histogram Mm. Den bästa metoden baserat på resultat, pålitlighet, antal bilder och utgivare är PNN klassificeraren. Metoden hade en sensitivitet på 94 % och en specificitet på 95 %. 300 bilder användes i experimentet som publicerades av IEEE år 2011. Metoder att diagnosticera diabetisk retinopati Morfologiska tekniker Luv colour space, Wiener filter and Canny edge detector. Den bästa metoden baserat på resultat, pålitlighet, antal bilder och utgivare är ett experimentet som heter ”Feature Extraction”. Experimentet inkluderar Luv colour space, Wiener filter (brus borttagning) och Canny edge detector

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The purpose of this master thesis was to investigate if using both X-ray images and patient's register data could increase the performance of a neural network in discrimination of two types of fractures in the thigh bone, called atypical femoral fractures (AFF) and normal femoral fractures (NFF). We also examined and evaluated how the fusion of the two data types could be done and how different types of fusion affect the performance. Finally, we evaluated how the number of variables in the register data affect a network's performance. Our image dataset consisted of 1,442 unique images from 580 patients (16.85% of the images were labelled AFF corresponding to 15.86% of the patients). Since the dataset is very imbalanced, sensitivity is a prioritized evaluation metric. The register data network was evaluated using five different versions of register data parameters: two (age and sex), seven (binary and non-binary) and 44 (binary and non-binary). Having only age and sex as input resulted in a classifier predicting all samples to class 0 (NFF), for all tested network architectures. Using a certain network structure (celled register data model 2), in combination with the seven non-binary parameters outperforms using both two and 44 (both binary and non-binary) parameters regarding mean AUC and sensitivity. Highest mean accuracy is obtained by using 44 non-binary parameters. The seven register data parameters have a known connection to AFF and includes age and sex. The network with X-ray images as input uses a transfer learning approach with a pre-trained ResNet50-base. This model performed better than all the register data models, regarding all considered evaluation metrics.        Three fusion architectures were implemented and evaluated: probability fusion (PF), feature fusion (FF) and learned feature fusion (LFF). PF concatenates the prediction provided from the two separate baseline models. The combined vector is fed into a shallow neural network, which are the only trainable part in this architecture. FF fuses a feature vector provided from the image baseline model, with the raw register data parameters. Prior to the concatenation both vectors were normalized and the fused vector is then fed into a shallow trainable network. The final architecture, LFF, does not have completely frozen baseline models but instead learns two separate feature vectors. These feature vectors are then concatenated and fed into a shallow neural network to obtain a final prediction. The three fusion architectures were evaluated twice: using seven non-binary register data parameters, or only age and sex. When evaluated patient-wise, all three fusion architectures using the seven non-binary parameters obtain higher mean AUC and sensitivity than the single modality baseline models. All fusion architectures with only age and sex as register data parameters results in higher mean sensitivity than the baseline models. Overall, probability fusion with the seven non-binary parameters results in the highest mean AUC and sensitivity, and learned feature fusion with the seven non-binary parameters results in the highest mean accuracy.