Unbiased noise estimation and denoising in parallel magnetic resonance imaging
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Unbiased noise estimation and denoising in parallel magnetic resonance imaging (348 views visite) (PDF restricted privato 231 views visite)

Borrelli P, Palma G, Comerci M, Alfano B

Icassp - Ieee International Conference On Acoustics - Speech And Signal Processing - Proceedings (ISSN: 1520-6149, 1520-61499781479928927), 2014 May; 10.1109/ICASSP.201: 1230-1234.



Paper type Tipo di articolo: Journal Article, Conference Paper,

Impact factor: 0, 5-year impact factor Impact factor a 5 anni: 0

Url: http://www.scopus.com/inward/record.url?eid=2-s2.0-84905234778&partnerID=40&md5=a99a24742c5bc0ee03b55ff111eb2070

Keywords Parole chiave: Denoising, Noise Estimation, Non-Local Means, Parallel Mri, Rician Noise, Algorithms, Image Denoising, Magnetic Resonance Imaging, Signal Processing, De-Noising, Acoustic Noise,

Affiliations Affiliazioni:

*** IBB - CNR ***
University of Naples Federico II, Dept. of Advanced Biomedical Sciences, Via Pansini 5, 80131 Naples, Italy
National Research Council of Italy, Inst. of Biostructures and Bioimaging, Via De Amicis 95, 80145 Naples, Italy
Universita degli Studi di Napoli Federico II
Consiglio Nazionale delle Ricerche


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Aja-Fern ndez, S., Trist n-Vega, A., Alberola-L pez, C., Noise estimation in single-and multiple-coil magnetic resonance data based on statistical models (2009) Magnetic Resonance Imaging, 27 (10), pp. 1397-1409

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Pruessmann, K. P., Encoding and reconstruction in parallel mri (2006) NMR in Biomedicine, 19 (3), pp. 288-299

Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A., Generalized autocalibrating partially parallel acquisitions (grappa) (2002) Magnetic Resonance in Medicine, 47 (6), pp. 1202-1210

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Manj n, J. V., Coup, P., Mart -Bonmat, L., Louis Collins, D., Robles, M., Adaptive nonlocal means denoising of mr images with spatially varying noise levels (2010) Journal of Magnetic Resonance Imaging, 31 (1), pp. 192-203



In magnetic resonance (MR) clinical practice, noise estimation is usually performed on Rayleigh-distributed background (no signal area) of magnitude images. Although noise variance in quadrature MR images is considered spatially independent, parallel MRI (pMRI) techniques as SENSE or GRAPPA generate spatially varying noise (SVN) distribution. In this scenario noise estimation from background may produce biased results. To address these limitations we introduce a novel noise estimation scheme based on local statistics. Our method turns out to be more accurate than the other pMRI noise estimation schemes previously described in the literature. Denoising performances, measured by visual inspection and peak signal-to-noise ratio (PSNR), of Non-Local Means denoising filters (NLM) are considerably improved using SVN-NLM in case of inhomogeneous noise. Furthermore, SVN-NLM behaves as well as standard NLM when homogeneous noise was added, thus proving to be a robust and powerful denoising algorithm for arbitrary MRI datasets. © 2014 IEEE.
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