Hampus Möller

Ph.D. student, currently working on machine learning methods for Magnetic Resonance Elastography (MRE) imaging techniques, primarily for use with the brain. My work focuses on the inversion procedure; extracting the mechanical information from measured data.

MRE aims to quantify and characterize mechanical properties of tissues such as the shear modulus, providing insight into the effects of brain diseases. In MRE, vibrations are introduced into the brain using low frequency vibrations, from which the tissue displacement can be measured using magnetic resonance imaging.

The measurement is followed by the so-called inversion. It is an inverse problem, meaning one infers system parameters from the system output and is typically hard to solve.

My interest lies in finding efficient ways to extract useful information hidden in data, primarily with machine learning. By efficient analysis of data one can accelerate understanding and potentially unlock important new insights into diseases, or into nature itself.