  Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue
micro-architecture, which can, in turn, be used to reconstruct tissue
microstructure and white matter pathways. The accuracy of such tasks is
hampered by the low signal-to-noise ratio in dMRI. Today, the noise is
characterized mainly by visual inspection of residual maps and estimated
standard deviation. However, it is hard to estimate the impact of noise on
downstream tasks based only on such qualitative assessments. To address this
issue, we introduce a novel metric, Noise Uncertainty Quantification (NUQ), for
quantitative image quality analysis in the absence of a ground truth reference
image. NUQ uses a recent Bayesian formulation of dMRI models to estimate the
uncertainty of microstructural measures. Specifically, NUQ uses the maximum
mean discrepancy metric to compute a pooled quality score by comparing samples
drawn from the posterior distribution of the microstructure measures. We show
that NUQ allows a fine-grained analysis of noise, capturing details that are
visually imperceptible. We perform qualitative and quantitative comparisons on
real datasets, showing that NUQ generates consistent scores across different
denoisers and acquisitions. Lastly, by using NUQ on a cohort of schizophrenics
and controls, we quantify the substantial impact of denoising on group
differences.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning