Quantitative Magnetic Resonance Imaging of Anisotropic Biological Tissues

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Magnetic resonance imaging (MRI) has become a powerful tool for the investigation of the central nervous system. Intense efforts are underway to develop quantitative MRI methods specific for particular components of biological tissue. Over the past five years, my students and I have investigated how quantitative MRI methods, such as myelin water imaging or perfusion image depend on tissue composition and tissue architecture and its orientation relative to the main magnetic field. We found that perfusion measurements are strongly influenced by tissue orientation since about half of the blood in the brain is in blood vessels that run in parallel with the brain's nerve fibre tracts. Depending on the MRI technique, the effect is up to 100% difference between white matter tissue that runs parallel to the magnetic field and tissue that runs perpendicular to the magnetic field. We developed numerical methods that allowed us to determine the blood volume in the brain's white matter, with high agreement between positron emission tomography (the gold standard) and our MRI approaches. For the myelin water fraction determined with a multi echo spin echo scan, we also found a systematic orientation effect of 30% between the lowest values and the highest values. The reasons for this effect are not clear. Dipole-dipole interactions as well as the magnetic susceptibility of the myelin sheath could contribute to the effect. During the next five years of my research program I will continue to investigate these orientation effects in order to contribute to our understanding of MRI signal generation. I will analyse myelin water data acquired in the non-myelinated human newborn brain and in myelinated and demyelinated post mortem tissue before and after fixation and develope numerical tissue models. From this work we will gain insights into the role of myelin on the orientation effects and how tissue fixation may have obscured orientation effects in previous post mortem studies. Furthermore, we have embarked on machine learning approaches to recover meaningful information from MRI scans. Normally, machine learning relies on large amounts of training data. With our understanding of magnetic resonance image generation, we are in the position to compute arbitrary amounts of synthetic training data. Neural networks trained on such synthetic data are able to solve problems encountered in real data, often with no or minimal additional training on real data. We will train two neural networks that will help us overcome barriers to the broader applicability of our quantitative methods. One network will determine tissue orientation from a conventional anatomical scan that is part of almost every brain MRI protocol. The other network will learn the noise distribution of the data in order to perform much improved parameter inference.