Magnetic resonance imaging is a diagnostic method to form images of the organs in the body. Long acquisition times are the main disadvantage, however it is possible to accelerate the data acquisition with the method of compressed sensing by sensing fewer samples and formulating an optimization method for image reconstruction. The aim of this thesis is to describe and compare the common optimization methods and to create a software capable of solving them. Another objective is to observe how much the data acquisition can be accelarated without the loss of image quality when dealing with real data. The most promising method in the experiment was total generalized variation (TGV) regularization which was able to reconstruct an image with a proper quality using only a quarter of the data.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:382192 |
| Date | January 2018 |
| Creators | Onderlička, Tomáš |
| Contributors | Šorel,, Michal, Rajmic, Pavel |
| Publisher | Vysoké učení technické v Brně. Fakulta strojního inženýrství |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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