Non-stationary noise estimation in MRI
These codes allow estimating spatially variant noise patterns from magnetic resonance imaging (MRI) data. Spatially variant noise components can be observed in MRI data acquired with parallel accelerated techniques like SENSE (SENSitivity Encoding) and GRAPPA (GeneRalized Autocalibrating Partial Parallel Acquisition). The methods estimate noise patterns using only a single magnitude MRI slice without any additional data required (e.g., sensitivity coil profiles, biophysical model of the data, repeated acquisitions).
- Spatially variant Rician/Gaussian noise estimation (homomorphic approach) [Matlab central]:
Aja-Fernández, S., Pieciak, T., Vegas-Sánchez-Ferrero, G. “Spatially variant noise estimation in MRI: A homomorphic approach”. Medical Image Analysis, 20(1), 184-197, 2015,
- Variance-stabilizing transformation for Rician distributed data and spatially variant Rician noise estimation [Matlab]:
Pieciak, T., Vegas-Sánchez-Ferrero, G., Aja-Fernández, S. “Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach”. IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2016.2625789