Spatially variant noise estimation in MRI: a homomorphic approach
- PMID: 25499191
- DOI: 10.1016/j.media.2014.11.005
Spatially variant noise estimation in MRI: a homomorphic approach
Abstract
The reliable estimation of noise characteristics in MRI is a task of great importance due to the influence of noise features in extensively used post-processing algorithms. Many methods have been proposed in the literature to retrieve noise features from the magnitude signal. However, most of them assume a stationary noise model, i.e., the features of noise do not vary with the position inside the image. This assumption does not hold when modern scanning techniques are considered, e.g., in the case of parallel reconstruction and intensity correction. Therefore, new noise estimators must be found to cope with non-stationary noise. Some methods have been recently proposed in the literature. However, they require multiple acquisitions or extra information which is usually not available (biophysical models, sensitivity of coils). In this work we overcome this drawback by proposing a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image. In the derivation, we considered the noise to follow a non-stationary Rician distribution, since it is the most common model in real acquisitions (e.g., SENSE reconstruction), though it can be easily generalized to other models. The proposed approach makes use of a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is then estimated by a low pass filtering with a Rician bias correction. Results in real and synthetic experiments evidence the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods.
Keywords: Homomorphic filtering; Noise estimation; Non-stationarity noise; Parallel imaging; SENSE.
Copyright © 2014 Elsevier B.V. All rights reserved.
Similar articles
-
Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach.IEEE Trans Pattern Anal Mach Intell. 2017 Oct;39(10):2015-2029. doi: 10.1109/TPAMI.2016.2625789. Epub 2016 Nov 7. IEEE Trans Pattern Anal Mach Intell. 2017. PMID: 27845653
-
Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach.IEEE Trans Image Process. 2008 Aug;17(8):1383-98. doi: 10.1109/TIP.2008.925382. IEEE Trans Image Process. 2008. PMID: 18632347
-
Adaptive non-local means denoising of MR images with spatially varying noise levels.J Magn Reson Imaging. 2010 Jan;31(1):192-203. doi: 10.1002/jmri.22003. J Magn Reson Imaging. 2010. PMID: 20027588
-
Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters.Magn Reson Imaging. 2014 Jul;32(6):702-20. doi: 10.1016/j.mri.2014.03.004. Epub 2014 Mar 18. Magn Reson Imaging. 2014. PMID: 24746774
-
Automatic estimation of the noise variance from the histogram of a magnetic resonance image.Phys Med Biol. 2007 Mar 7;52(5):1335-48. doi: 10.1088/0031-9155/52/5/009. Epub 2007 Feb 8. Phys Med Biol. 2007. PMID: 17301458 Review.
Cited by
-
Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling.Magn Reson Med. 2020 Nov;84(5):2523-2536. doi: 10.1002/mrm.28314. Epub 2020 May 19. Magn Reson Med. 2020. PMID: 32424947 Free PMC article.
-
Stimulated-echo diffusion-weighted imaging with moderate b values for the detection of prostate cancer.Eur Radiol. 2020 Jun;30(6):3236-3244. doi: 10.1007/s00330-020-06689-w. Epub 2020 Feb 16. Eur Radiol. 2020. PMID: 32064561
-
Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT.Neuroimage. 2016 Feb 15;127:456-471. doi: 10.1016/j.neuroimage.2015.10.034. Epub 2015 Oct 22. Neuroimage. 2016. PMID: 26499810 Free PMC article.
-
Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.Biometrics. 2024 Oct 3;80(4):ujae116. doi: 10.1093/biomtc/ujae116. Biometrics. 2024. PMID: 39468741
-
Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.IEEE Trans Med Imaging. 2019 Dec;38(12):2838-2848. doi: 10.1109/TMI.2019.2915629. Epub 2019 May 8. IEEE Trans Med Imaging. 2019. PMID: 31071025 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical