Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: Applications to DT-MRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 171-179

  Wiest Daesslé, Nicolas ^a,b   Prima, Sylvain ^a,b   Coupé, Pierrick ^a,b   Morrissey, Sean Patrick ^a,b,c   Barillot, Christian ^a,b  

^a INRIA   (France)

^b UNIVERSITÉ DE RENNES 1   (France)

^c RENNES UNIVERSITY HOSPITAL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION; IMAGE DENOISING; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; RESTORATION; TENSORS;

DIFFUSION-WEIGHTED MRI; HIGH ANGULAR RESOLUTION DIFFUSION IMAGING; HIGH SIGNALTO-NOISE RATIOS (SNR); LOW SIGNAL-TO-NOISE RATIO; NON-LOCAL MEANS FILTERING; PEAK SIGNAL TO NOISE RATIO; RESTORATION OF IMAGES; STATE-OF-THE-ART METHODS;

SIGNAL TO NOISE RATIO;

ALGORITHM; ARTICLE; ARTIFACT; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFACTS; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84883838668     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_21     Document Type: Conference Paper

References (22)

1. Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An Optimized Blockwise Non Local Means Denoising Filter for 3D Magnetic Resonance Images. Transactions on Medical Imaging 27, 425-441 (2008)
2. Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C.: Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 344-351. Springer, Heidelberg (2007)
3. Sijbers, J., den Dekker, A.J.: Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magn. Reson. Med. 51, 586-594 (2004)
4. Koay, C.G., Chang, L.C., Carew, J.D., Pierpaoli, C., Basser, P.J.: A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J. Magn. Reson. 182, 115-125 (2006)
5. Chang, L.C., Jones, D.K., Pierpaoli, C.: RESTORE: robust estimation of tensors by outlier rejection. Magn. Reson. Med. 53, 1088-1095 (2005)
6. Tschumperlé, D., Deriche, R.: Variational frameworks for DT-MRI estimation, regularization and visualization. In: ICCV 2003, pp. 116-121 (2003)
7. Coulon, O., Alexander, D.C., Arridge, S.: Diffusion tensor magnetic resonance image regularization. Med. Image Anal. 8, 47-67 (2004)
8. Chefd'hotel, C., Tschumperlé, D., Deriche, R., Faugeras, O.: Regularizing flows for constrained matrix-valued images. Journal of Mathematical Imaging and Vision 20, 147-162 (2004)
9. Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. International Journal of Computer Vision 66, 41-66 (2006)
10. Castaño-Moraga, C., Lenglet, C., Deriche, R., Ruiz-Alzola, J.: A Riemannian approach to anisotropic filtering of tensor fields. Signal Processing 87, 263-276 (2007)
11. Fillard, P., Pennec, X., Arsigny, V., Ayache, N.: Clinical DT-MRI estimation, smoothing, and fiber tracking with log-Euclidean metrics. IEEE TMI 26, 1472-1482 (2007)
12. Landman, B., Bazin, P.L., Prince, J.: Diffusion Tensor Estimation by Maximizing Rician Likelihood. In: Lew, M., Sebe, N., Huang, T., Bakker, E. (eds.) ICCV 2007, Rio de Janeiro, Brazil, pp. 1-8 (2007)
13. Basu, S., Fletcher, P.T., Whitaker, R.T.: Rician Noise Removal in Diffusion Tensor MRI. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 117-125. Springer, Heidelberg (2006)
14. Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation 4, 490-530 (2005)
15. Kervrann, C., Boulanger, J., Coupé, P. (eds.): Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal, pp. 520-532. Springer, Heidelberg (2007)
16. Gasser, T., Sroka, L., Steinmetz, C.: Residual variance and residual pattern in non linear regression. Biometrika 73, 625-633 (1986)
17. Stejskal, E.O., Tanner, J.E.: Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient. The Journal of Chemical Physics 42, 288-292 (1965)
18. Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Fast and simple calculus on tensors in the Log-Euclidean framework. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 115-122. Springer, Heidelberg (2005)
19. Manjón, J.V., Carbonell-Caballero, J., Lull, J.J., García-Martí, G., Martí-Bonmatí, L., Robles, M.: MRI denoising using Non-Local Means. Medical Image Analysis (in Press, Corrected Proof 2008)
20. Keiko, T., Satomi, K., Masahiro, I., Sadao, S., Yutaka, A., Kunihiko, F.: Usefulness of high-b-value diffusion-weighted imaging in acute cerebral infarction. European Radiology 17, 1212-1220 (2007)
21. Yoshiura, T., Mihara, F., Tanaka, A., Ogomori, K., Ohyagi, Y., Taniwaki, T., Yamada, T., Yamasaki, T., Ichimiya, A., Kinukawa, N., Kuwabara, Y., Honda, H.: High b value diffusion-weighted imaging is more sensitive to white matter degeneration in Alzheimer's disease. Neurolmage 20, 413-419 (2003)
22. Descoteaux, M., Wiest-Daesslé, N., Prima, S., Barillot, C., Deriche, R.: Impact of Rician Adapted Non-Local Means Filtering on HARDI. In: Metaxas, D., et al. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 122-130. Springer, Heidelberg (2008)