Project Summary This supplement aims to disseminate the NORDIC (NOise Reduction with DIstribution Corrected) PCA method for reducing the influence of thermal noise in neuroimaging data. NORDIC uses a dedicated processing approach to ensure that the noise component is additive with independent, identically distributed, zero-mean Gaussian entries. Using this characterization, results from random matrix theory can be efficiently used to devise a parameter-free objective threshold. For NORDIC, this threshold value is both numerically quantifiable and descriptive as the removal of components which cannot be distinguished from Gaussian noise. NORDIC, unlike other methods, uses known information from the acquisition to transform the data to fit the algorithm instead of either estimating the necessary information or adapting the algorithm to fit the data. This approach for denoising is unique from previous methods as it has negligible, if any, impact on real MR signals and can be more generally applied to different types of MRI data without re-calibration or optimization. This, in turn, allows for much higher resolutions and/or reduced scan times of otherwise SNR-starved MRI protocols. The cost of the method is no more than having a clean sampling of the noise and modest computational requirements, which could be implemented into an online acquisition protocol and reconstruction pipeline for any protocol from any MRI scanner. The NORDIC code will be made available to users and we will solicit feedback from a group of end users with the goal of further optimizing and testing the denoising technique.