Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means

By Yaniv Gal, Andrew J. H. Mehnert, Andrew P. Bradley, Kerry McMahon, Dominic Kennedy, Stuart Crozier. 2010

89 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Yanhui Guo
2. J. Mohan
3. V. Krishnaveni
4. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Florian Tramèr
3. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “The qualitative and quantitative (MSE) denoising results were better than spatiotemporal Beltrami, moving average, the dynamic Non Local Means method [182], and stacked denoising autoencoders [183].”
• A survey on the magnetic resonance image denoising methods (2014, methodology): “[33] proposed the dynamic nonlocal means (DNLM), for denoising Dynamic Contrast Enhanced MR images.”