Although improved treatments have reduced the rates of acute complications from myocardial infarction, sequelae such as heart failure and sudden death threaten the future wellbeing of those patients. Secondary prevention after myocardial infarction is related to cardiovascular risk factors and the effect of the infarct on left ventricular function. Cardiovascular magnetic resonance imaging (CMR) is necessary to determine the size of the infarct scar and can with great precision determine left ventricular volumes, left ventricular ejection fraction, and deformation (strain and torsion). The purpose of this thesis was to improve on CMR methods to facilitate image acquisition and post processing in patients with high risk of coronary artery disease (CAD). In Paper 1, a three-dimensional phase-sensitive inversion-recovery (3D PSIR) sequence was modified to measure T1 during a single breath hold. The measured T1 values were used to extrapolate a map of T1 relaxation, which avoided the time-consuming manual determination of the inversion time. The data collection consisted of phantom experiments, Monte Carlo simulations of the effect of various heart rates, and clinical investigation of 18 patients with myocardial infarction. Scar images created with the modified sequence were compared to those created with the standard sequence. The 3D PSIR sequence was able to measure T1 relaxation with a high accuracy up to 800 ms, which is in the suitable range for scar imaging. Simulated arrhythmias showed that the method was robust and able to tolerate some variation in heart rate. The modified sequence provides measurements of inversion time that can be used to facilitate standard scar imaging or to reconstruct synthetic scar images. Images of infarct scar obtained with the 3D PSIR sequence bore striking similarity to images obtained with the standard sequence. In Paper 2, 125 patients with high risk of CAD were investigated using the displacement encoding with stimulated echoes (DENSE) sequence. Image segments with infarct scar area >50% (transmurality) could be identified with a sensitivity of 95% and a specificity of 80% based on circumferential strain calculated from the DENSE measurements. The DENSE sequence was also applied in other directions, but its sensitivity and specificity to detect scar was lower than when used for circumferential strain. In Paper 3, 90 patients with high risk of CAD were examined by DENSE, tagging with harmonic phase (HARP) imaging and cine imaging with feature tracking (FT), to detect cardiac abnormalities as manifested in end-systolic circumferential strain. Circumferential strain calculated with DENSE had higher sensitivity and specificity than the competing methods to detect infarction with transmurality >50%. Global circumferential strain measured by DENSE correlated better with global parameters such as left ventricular ejection fraction, myocardial wall mass, left ventricular end-diastolic and end-systolic volume; than strain measured by FT or HARP. In Paper 4, myocardial torsion was investigated using DENSE, HARP, and FT in 48 patients with high risk of CAD. Torsion measured by each of the three methods was correlated with other global measures such as left ventricular ejection fraction, left ventricular mass, and left ventricular end-diastolic and end-systolic volumes. The torsion measurements obtained with DENSE had a stronger relationship with left ventricular ejection fraction, left ventricular mass, and volumes than those obtained with HARP or FT. DENSE was superior to the other methods for strain and torsion measurement and can be used to describe myocardial deformation quantitatively and objectively.

Magnetic Resonance Imaging (MRI) has a high soft-tissue contrast with a high sensitivity for detecting pathological changes in the brain. Conventional MRI is a time-consuming method with multiple scans that relies on the visual assessment of the neuroradiologist. Synthetic MRI uses one scan to produce conventional images, but also quantitative maps based on relaxometry, that can be used to quantitatively analyse tissue properties and pathological changes. The studies presented here apply the use of synthetic MRI of the brain in different clinical settings. In the first study, synthetic MR images were compared to conventional MR images in 22 patients. The contrast, the contrast-to-noise ratio, and the diagnostic quality were assessed. Image quality was perceived to be inferior in the synthetic images, but synthetic images agreed with the clinical diagnoses to the same extent as the conventional images. Patients with early multiple sclerosis were analysed in the second study. In patients with multiple sclerosis, contrast-enhancing white matter lesions are a sign of active disease and can indicate a need for a change in therapy. Gadolinium-based contrast agents are used to detect active lesions, but concern has been raised regarding the long-term effects of repeated use of gadolinium. In this study, relaxometry was used to evaluate whether pre-contrast injection tissue-relaxation rates and proton density can identify active lesions without gadolinium. The findings suggest that active lesions often have relaxation times and proton density that differ from non-enhancing lesions, but with some overlap. This makes it difficult to replace gadolinium-based contrast agent injection with synthetic MRI in the monitoring of MS patients. Malignant gliomas are primary brain tumours with contrast enhancement due to a defective blood-brain barrier. However, they also grow in an infiltrative, diffuse manner, making it difficult to clearly delineate them from surrounding normal brain tissue in the diagnostic workup, at surgery, and during follow-up. The contrast-enhancing part of the tumour is easily visualised, but not the diffuse infiltration. In studies three and four, synthetic MRI was used to analyse the peritumoral area of malignant gliomas, and revealed quantitative findings regarding peritumoral relaxation changes and non-visible contrast enhancement suggestive of non-visible infiltrative tumour growth. In conclusion, synthetic MRI provides quantitative information about the brain tissue and this could improve the diagnosis and treatment for patients.

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No description available.

Background To combat one of the leading causes of death worldwide, lung cancer treatment techniques and modalities are advancing, and the treatment options are becoming increasingly individualized. Modern cancer treatment includes the option for the patient to be treated with proton therapy, which can in some cases spare healthy tissue from excessive dose better than conventional photon radiotherapy. However, to assess the benefit of proton therapy compared to photon therapy, it is necessary to make both treatment plans to get information about the Tumour Control Probability (TCP) and the Normal Tissue Complication Probability (NTCP). This requires excessive treatment planning time and increases the workload for planners.  Aim This project aims to investigate the possibility for automated prediction of the treatment dose distribution using a deep learning network for lung cancer patients treated with photon radiotherapy. This is an initial step towards decreasing the overall planning time and would allow for efficient estimation of the NTCP for each treatment plan and lower the workload of treatment planning technicians. The purpose of the current work was also to understand which features of the input data and training specifics were essential for producing accurate predictions.  Methods Three different deep learning networks were developed to assess the difference in performance based on the complexity of the input for the network. The deep learning models were applied for predictions of the dose distribution of lung cancer treatment and used data from 95 patient treatments. The networks were trained with a U-net architecture using input data from the planning Computed Tomography (CT) and volume contours to produce an output of the dose distribution of the same image size. The network performance was evaluated based on the error of the predicted mean dose to Organs At Risk (OAR) as well as the shape of the predicted Dose-Volume Histogram (DVH) and individual dose distributions.  Results  The optimal input combination was the CT scan and lung, mediastinum envelope and Planning Target Volume (PTV) contours. The model predictions showed a homogenous dose distribution over the PTV with a steep fall-off seen in the DVH. However, the dose distributions had a blurred appearance and the predictions of the doses to the OARs were therefore not as accurate as of the doses to the PTV compared to the manual treatment plans. The performance of the network trained with the Houndsfield Unit input of the CT scan had similar performance as the network trained without it.  Conclusions As one of the novel attempts to assess the potential for a deep learning-based prediction model for the dose distribution based on minimal input, this study shows promising results. To develop this kind of model further a larger data set would be needed and the training method could be expanded as a generative adversarial network or as a more developed U-net network.

Background. Classification based PET/MRI attenuation correction methods utilize a PET/CT derived method of identifying tissues and assigning them attenuation values. Phantoms for PET/MRI generally use theoretical attenuation maps.Aim. This paper investigates the creation of fluids that can be used in phantoms for PET, CT, and MRI. The focus lies on creating properties that result in the attenuation to be correctly estimated with CT and MRI systems. This is a step toward the development of hybrid phantoms with tissue equivalency in all modalities.Method. Attenuation and fat fraction is altered in water to produce a fluid that generates correct attenuation values when measured with both CT and MRI. The fat fraction influences what type of tissue the fluid is classified as in the PET/MRI system used. In turn, the tissue type has an attenuation value associated with it. An emulsion of vegetable oil and water is investigated for the manipulation of fat fractions. The CT attenuation measurement in Hounsfield is modified by altering the density of the fluid. The concentration of sodium chloride is used to modify the Hounsfield value. In the end, the phantom of oil, water, salt solution, and emulsion is validated by adding PET-isotopes. The measured attenuation-corrected radioactivity concentration in PET/CT and PET/MRI are compared.Results. The results show that modification of salt concentration is an effective and easily applicable method of altering the Hounsfield attenuation value of a liquid. The use of emulsions results in expected classification based on fat fraction. Long- and short-term stability of the emulsions may be improved. Analysis of radioactivity concentration shows comparable values between PET/CT and PET/MRI proving that the concept works. / Bakgrund. Klassificeringsbaserad attenueringskorrektion i PET/MR utnyttjar en metod för PET/CT där vävnad identifieras och tilldelas ett attenueringsvärde. Fantomer för PET/MR använder vanligtvis teoretiska attenueringskartor.Mål. Den här rapporten undersöker skapandet av vätskor som kan användas i fantomer för PET, CT och MR. Fokuset ligger på att skapa egenskaper som ger samma korrekta resultat för attenuering i både CT- och MR-system. Detta är ett steg mot utvecklingen av hybridfantom med vävnadsekvivalens för alla modaliteter.Metod. Attenuering och fettfraktion ändras i vatten för att skapa en vätska som ger korrekta attenuringsvärden i både CT och MR. Fettfraktionen bestämmer vilken typ av vävnad som vätskan klassificeras som i det använda PET/MR-systemet. Vävnadsklassificeringen har ett associerat attenueringsvärde. En emulsion av vegetabilisk olja och vatten undersöks för manipulering av fettfraktioner. Attenueringsvärdet som mätt av en CT modifieras genom att ändra densiteten på vätskan. Koncentrationen av natriumklorid ändras för att påverka Hounsfield-attenueringsvärdet. Slutligen så undersöks fantomet med olja, vatten, saltlösning och en emulsion genom tillsatsen av PET-isotoper. Den attenueringskorrigerade radioaktiva koncentrationen som uppmättes i PET/CT och PET/MR jämförs.Resultat. Resultaten visar att modifikation av saltkoncentration är en effektiv och enkelt tillämpbar metod för att påverka Hounsfield-värdet på en vätska. Användningen av emulsioner leder till en förväntad klassificering baserad på fettfraktionen. Lång- och kortvarig stabilitet av emulsionerna kan förbättras. En analys av radioaktivitetskoncentrationen visar jämförbara värden mellan PET/CT och PET/MR vilket visar att konceptet fungerar.

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Abstract Nuclear Medicine (NM) images inherently suffer from large amounts of noise and blur. The purpose of this research is to reduce the noise and blur while maintaining image integrity for improved diagnosis. The proposal is to further improve image quality after the standard pre- and post-processing undertaken by a gamma camera system. Mean Field Annealing (MFA), the image processing technique used in this research is a well known image processing approach. The MFA algorithm uses two techniques to achieve image restoration. Gradient descent is used as the minimisation technique, while a deterministic approximation to Simulated Annealing (SA) is used for optimisation. The algorithm anisotropically diffuses an image, iteratively smoothing regions that are considered non-edges and still preserving edge integrity until a global minimum is obtained. A known advantage of MFA is that it is able to minimise to this global minimum, skipping over local minima while still providing comparable results to SA with significantly less computational effort. Image blur is measured using either a point or line source. Both allow for the derivation of a Point Spread Function (PSF) that is used to de-blur the image. The noise variance can be measured using a flood source. The noise is due to the random fluctuations in the environment as well as other contributors. Noisy blurred NM images can be difficult to diagnose particularly at regions with steep intensity gradients and for this reason MFA is considered suitable for image restoration. From the literature it is evident that MFA can be applied successfully to digital phantom images providing improved performance over Wiener filters. In this paper MFA is shown to yield image enhancement of planar NM images by implementing a sharpening filter as a post MFA processing technique.

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