Component Analysis based on Standard-deviation Attenuation (CASA): a new algorithm for the denoising of Diffusion MRI data

Noise is a crucial problem that affects even the most advanced MRI techniques based on model fitting. It is therefore important to act on the raw data to remove as much noise as possible, while preserving the anatomical structures. Many techniques based on Principal Component Analysis (PCA) take advantage of the redundancy of information contained in multiphase data, to perform robust denoising. In this work, we introduce a new denoising method based on PC images. We show the added value of our technique on both the raw data and the derived fractional anisotropy map.