Diffusion MRI noise mapping using random matrix theory

By Jelle Veraart, Els Fieremans, Dmitry S. Novikov. Magnetic Resonance in Medicine, 2016

781 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/51a816c34e7539674edab4dc2e2b89585ff2ce55

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Daan Christiaens

In Radiology, Nuclear Medicine and Imaging

1. Daan Christiaens
2. Benjamin Ades-aron
3. Joseph V. Hajnal
4. Jan Sijbers
5. Jelle Veraart
6. Els Fieremans
7. Anthony N. Price
8. Dmitry S. Novikov

Citations in the corpus

• Complex diffusion-weighted image estimation via matrix recovery under general noise models (2018, positive): “Further contributions have proposed to estimate R by resorting to random matrix theory, namely to the MarčenkoPastur law that governs the empirical spectral noise distribution, both for DWI noise estimation [8] and denoising [9].”
• Denoising of diffusion MRI using random matrix theory (2016, methodology): “The noise level, being an additional product of the method (Veraart et al., 2016), offers the opportunity to correct the denoised signal for Rician or noncentral-χ distributed noise bias (Aja-Fernández et al.”