Quantifying MRI: from reconstruction to application in neuroimaging

Abstract: To understand the origin and development of neurological disorders, Magnetic Resonance Imaging (MRI) has proven to provide valuable quantitative measures. In this overview presentation, I will touch upon several topics. Volumetric measures obtained via segmentation find their application in prenatal famine exposure, HIV and ischemic stroke. We developed a scattering transform that has no learnable parameters for computing convolutional neural networks in small patient studies. Diffusion MRI is a sensitive method for detecting microstructural changes. We assessed the reproducibility of different models in a multi-site context using a complex diffusion phantom. T1, T2* and Quantitative Susceptibility Mapping (QSM) allow for mapping myelin and iron content at high resolution at 7T. We present a single MRI sequence for obtaining these measures in a time efficient manner. Further acceleration may be achieved by compressed sensing and potentially deep learning.