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A black and white reflection of Vietnam : En svartvit reflektion av VietnamEdlund, Carola January 2005 (has links)
No description available.
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Segmentation methods and shape descriptions in digital images : applications in 2D and 3D microscopy /Sintorn, Ida-Maria, January 2005 (has links) (PDF)
Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2005. / Härtill 8 uppsatser.
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Fuzzy similarity-based image processing /Tolt, Gustav, January 2005 (has links)
Diss. Örebro : Örebro universitet, 2005.
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Multidimensional MRI of cardiac motion : acquisition, reconstruction and visualization /Sigfridsson, Andreas, January 2006 (has links)
Lic.-avh. (sammanfattning) Linköping : Linköpings universitet, 2006. / Härtill 3 uppsatser.
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Patientstråldosjämförelse vid 100 kV CT-pulmonalis och 80 kV CT-pulmonalis : En kvantitativ studieStröm, Mathilda, Karlsson, Sandra January 2018 (has links)
No description available.
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Magnetic Resonance Imaging of Myocardial Deformation and Scarring in Coronary Artery Disease.Kihlberg, Johan January 2017 (has links)
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.
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Clinical Applications of Synthetic MRI of the BrainBlystad, Ida January 2017 (has links)
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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Stråldoser vid barnundersökningar : en enkätstudieIbrahimovic, Almedina, Nyrén, Jonas January 2020 (has links)
No description available.
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Röntgensjuksköterskors syn på personalbristLkabous, Zakaria, Tanta, Muaid January 2020 (has links)
No description available.
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Deep-learning based prediction model for dose distributions in lung cancer patientsHellström, Terese January 2021 (has links)
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.
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