Sökning: "Magnetic Domains"
Visar resultat 1 - 5 av 11 uppsatser innehållade orden Magnetic Domains.
1. Enabling Hybrid Real Time and Retrospectively Gated Imaging in a Numerical Phantom
Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)Sammanfattning : Sector-Wise Golden Angle (SWIG) is a novel approach that was developed to address the limitations associated with Golden Angle radial imaging, commonly used for high temporal resolution flow measurements. Golden angle radial imaging is a time-efficient method that effectively reduces motion sensitivity. LÄS MER
2. Deep learning for temporal super-resolution of 4D Flow MRI
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : The accurate assessment of hemodynamics and its parameters play an important role when diagnosing cardiovascular diseases. In this context, 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique that facilitates hemodynamic parameter assessment as well as quantitative and qualitative analysis of three-directional flow over time. LÄS MER
3. Preparation and Characterization of Tb-Co Magnetic Films
L2-uppsats, Uppsala universitet/MaterialfysikSammanfattning : Thin films of terbium-cobalt alloy have been deposited by magnetron sputtering. They exhibit an out-of-plane magnetic anisotropy that may result in elongated domains. The effect of thicknesses and compositions on the domain structure has been studied systematically. LÄS MER
4. Assignment of heteronuclear methyl-NMR spectrum of the 44 kDa protein MALT1 Casp-IgL3
Master-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationSammanfattning : Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) plays an important role in the immune pathway that controls the activation and proliferation of B and T cells. Dysregularization of this pathway leads to the development of highly aggressive lymphomas. LÄS MER
5. Data Augmentation to Improve Cross-Domain Generalization in Deep Learning MRI Segmentation
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : Semantic segmentation of medical images is an important task with many applications. However, manually delineating 3D images is time-consuming and the demand for automation is high. For many image segmentation tasks, deep learning has provided state-of-the-art results. LÄS MER