Recent advances in neuro-imaging attempt to enable in-vivo histology of the brain. Doing so requires increased spatial resolution up to a situation where the signal meets the noise floor. The talk will cover research conducted at WIAS, in collaboration with MR physicists, on statistical issues in modeling imaging data characterized by low signal-to-noise ratio (SNR). I'll cover several specific, but interrelated problems: - characterization of the signal distribution in MR experiments, - effects of preprocessing on the signal distribution, - estimation of the noise profile in MR images, - use of spatial information for variance reduction in (collections of) MR images, - bias due to incorrect modeling in MR experiments. I'll consider two specific imaging experiments to illustrate problems, characterize effects that are due to high measurment noise and provide solutions for: - diffusion weighted MR, with an analysis based on data of the Human Connectome Project, - multi-parameter mapping, using data measured at the Wellcome Trust Center for Neuroimaging, London. Literature: S. Becker, K. Tabelow, S. Mohammadi, N. Weiskopf and J. Polzehl, Adaptive smoothing of multi-shell diffusion-weighted MR data by msPOAS, NeuroImage, 95 (2014) pp. 90--105. K. Tabelow, H.U. Voss and J. Polzehl, Local estimation of the noise level in MRI using structural adaptation, Medical Image Analysis, 20 (2015) pp. 76--86. J. Polzehl and K. Tabelow, Low SNR in dMRI models, JASA, 11 (2016) pp. 1480--1490. K. Tabelow, Ch. D'Alonzo, L. Ruthotto, M. F. Callaghan, N. Weiskopf, J. Polzehl and S. Mohammadi, Removing the estimation bias due to the noise floor in multi-parameter maps, ISMRM annual meeting 2017.