ETD

• Bayesian Optimization
• experimental design
• Gaussian Processes
• quantitative magnetic resonance imaging

The thesis concerns the automatic selection of parameters controlling sequence acquisition in quantitative magnetic resonance imaging. A Bayesian Optimization approach is proposed based on Gaussian Processes. The method has been tested using undersampled acquisition with pseudo-random lists of sequence parameters for the purpose of identifying an optimal schedule to be ultimately used in clinical imaging.