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Patient Motion Modeling for Robust Magnetic Resonance Imaging
Patient motion is one of the main causes of artifacts in MR imaging. The aim of this project is to develop image reconstruction algorithms that include data-driven motion compensation techniques.
An MR scan usually consists of multiple data segments that are acquired subsequently by the scanner. Patient motion can therefore affect the signal evolution throughout the scan in various ways. Most prominently motion leads to anatomical mismatches between the data packages, but it also changes the magnetic field conditions in the scanner and might even deteriorate the consistency of each individual data package itself.
An MR scan usually consists of multiple data segments that are acquired subsequently by the scanner. Patient motion can therefore affect the signal evolution throughout the scan in various ways. Most prominently motion leads to anatomical mismatches between the data packages, but it also changes the magnetic field conditions in the scanner and might even deteriorate the consistency of each individual data package itself.
The goal of this project is to design appropriate signal models that capture the motion-related variations and integrate them into dedicated image reconstruction algorithms. The project work thus might include – depending on your interest and experience – a combination of theoretical considerations, software implementations, simulation studies and experiments at our MRI scanners.
For this project, you should have some programming experience with a scientific computing programming language/framework (e.g. Matlab or Python with Numpy/Scipy). Prior knowledge about magnetic resonance imaging (e.g. from the Magnetic Resonance in Medicine course) is a big plus.
The goal of this project is to design appropriate signal models that capture the motion-related variations and integrate them into dedicated image reconstruction algorithms. The project work thus might include – depending on your interest and experience – a combination of theoretical considerations, software implementations, simulation studies and experiments at our MRI scanners.
For this project, you should have some programming experience with a scientific computing programming language/framework (e.g. Matlab or Python with Numpy/Scipy). Prior knowledge about magnetic resonance imaging (e.g. from the Magnetic Resonance in Medicine course) is a big plus.
Please contact Dr. Malte Riedel (riedel@biomed.ee.ethz.ch) or Thomas Ulrich (ulrich@biomed.ee.ethz.ch) if you are interested in hearing more about this topic and the different projects we can offer.
Supervising professor: Klaas Prüssmann (https://mrtm.ethz.ch/)
Please contact Dr. Malte Riedel (riedel@biomed.ee.ethz.ch) or Thomas Ulrich (ulrich@biomed.ee.ethz.ch) if you are interested in hearing more about this topic and the different projects we can offer. Supervising professor: Klaas Prüssmann (https://mrtm.ethz.ch/)
