Spatially variable Rician noise in magnetic resonance imaging

Medical Image Analysis

Volumn 16, Issue 2, 2012, Pages 536-548

  Maximov, Ivan I ^a   Farrher, Ezequiel ^a   Grinberg, Farida ^a   Jon Shah, N ^a,b  

^a FORSCHUNGSZENTRUM JÜLICH   (Germany)

^b RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Diffusion weighted imaging; Gaussian noise; Low signal to noise ratio; Rician noise; Spatially variable noise

Indexed keywords

BODY MOTIONS; COIL ARRAYS; CORRECTION FACTORS; DIFFUSION TENSOR IMAGING; DIFFUSION WEIGHTED IMAGING; EFFECTIVE TOOL; FIELD INHOMOGENEITY; GRADIENT COIL; HIGH SIGNAL-TO-NOISE RATIO; IN-VIVO; INHOMOGENEOUS DISTRIBUTION; LOW SIGNAL-TO-NOISE RATIO; MAGNETIC RESONANCE IMAGES; PHYSIOLOGICAL NOISE; RANDOM FACTORS; REPRODUCIBILITIES; RESPIRATORY MOTIONS; RICIAN NOISE; SIGNAL CORRECTION; SPATIALLY VARIABLE NOISE; STANDARD DEVIATION; STANDARD METHOD; UNIFORM DISTRIBUTION;

DIFFUSION; FADING (RADIO); GAUSSIAN NOISE (ELECTRONIC); SIGNAL TO NOISE RATIO; TENSORS;

MAGNETIC RESONANCE IMAGING;

ARTICLE; CLINICAL ASSESSMENT TOOL; DIFFUSION TENSOR IMAGING; EVALUATION; IN VIVO STUDY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; RICIAN NOISE; SIGNAL NOISE RATIO; SIMULATION; VOXEL BASED MORPHOMETRY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84856229439     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2011.12.002     Document Type: Article

References (46)

1. Abramowitz M., Stegun I.A. Handbook of Mathematical Functions 1974, Dover, Publications, New York.
2. Aja-Fernandez S., Tristan-Vega A., Alberola-Lopez C. Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models. Magn. Reson. Imag. 2009, 27:1397-1409.
3. Andersen A.H., Kirsch J.E. Analysis of noise in phase contrast MR images. Med. Phys. 1996, 23:857-869.
4. Anderson A.W. Theoretical analysis of the effects of noise on diffusion tensor imaging. Magn. Reson. Med. 2001, 46:1174-1188.
5. Andersson J.L.R. Maximum a posteriori estimation of diffusion tensor parameters using a Rician noise model: why, how and but. NeuroImage 2008, 42:1340-1356.
6. Brummer M.E., Mersereau R.M., Eisner R.L., Lewine R.R.J. Automatic detection of brain contours in MRI data sets. IEEE Trans. Med. Imag. 1993, 12:153-219.
7. Chang L.C., Jones D.K., Pierpaoli C. RESTORE: robust estimation of tensors by outlier rejection. Magn. Reson. Med. 2005, 53:1088-1095.
8. Chang L.C., Rohde G.K., Pierpaoli C. An automatic method for estimating noise-induced signal variance in magnitude-reconstructed magnetic resonance images. Proc. Soc. Photo. Opt. Inst. Eng. 2005, 5747:1136-1142.
9. Clark C.A., Le Bihan D. Water diffusion compartmentation and anisotropy at high b values in the human brain. Magn. Reson. Med. 2000, 44:852-859.
10. Clark C.A., Hedehus M., Moseley M.E. In vivo mapping of the fast and slow diffusion tensors in human brain. Magn. Reson. Med. 2002, 47:623-628.
11. Coupe P., Manjon J.V., Gedamu E., Arnold D., Robles M., Collins D.L. Robust Rician noise estimation for MR images. Med. Image Anal. 2010, 14:483-493.
12. Dietrich O., Heiland S., Sartor K. Noise correction for the exact determination of apparent diffusion coefficients at low SNR. Magn. Reson. Med. 2001, 45:448-453.
13. Giraudeau P., Mueller N., Jerschow A., Frydman L. H-1 NMR noise measurements in hyperpolarized liquid samples. Chem. Phys. Lett. 2010, 489:107-112.
14. Grinberg F., Farrher E., Kaffanke J., Oros-Peusquens, Shah N.J. Non-Gaussian diffusion in human brain tissue at high b-factors as examined by a combined diffusion kurtosis and biexponential diffusion tensor analysis. NeuroImage 2011, 57:1087-1102.
15. Gudbjartsson H., Patz S. The Rician distribution of noisy MRI data. Magn. Reson. Med. 1995, 34:910-914.
16. Henkelman R.M. Measurement of signal intensities in the presence of noise in MR images. Med. Phys. 1985, 12:232-233.
17. Jones D.K., Basser P.J. Squashing peanuts and smashing pumpkins: how noise distorts diffusion-weighted MR data. Magn. Reson. Med. 2004, 52:979-993.
18. 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. 2006, 182:115-125.
19. Koay C.G., Basser P.J. Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. J. Magn. Reson. 2006, 179:317-322.
20. Koay C.G., Oezarslan E., Basser P.J. A signal transformational framework for breaking the noise floor and its application in MRI. J. Magn. Reson. 2009, 197:108-119.
21. Koay C.G., Oezarslan E., Pierpaoli C. Probabilistic identification and estimation of noise (PIESNO): a self-consistent approach and its application in MRI. J. Magn. Reson. 2009, 199:94-103.
22. Koay C.G. Least squares approaches to diffusion tensor estimation. Diffusion MRI: Theory, Methods, and Applications 2011, Oxford University Press. D.K. Jones (Ed.).
23. Landman B.A., Bazin P.L., Smith S.A., Prince J.L. Robust estimation of spatially variable noise fields. Magn. Reson. Med. 2009, 62:500-509.
24. Landman B.A., Bazin P.L., Prince J.L. Estimation and application of spatially variable noise fields in diffusion tensor imaging. Magn. Reson. Imag. 2009, 27:741-751.
25. Makovski A. Noise in MRI. Magn. Reson. Med. 1996, 36:494-497.
26. Manjon J.V., Coupe P., Marti-Bonmati L., Collins L., Robles M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imag. 2010, 31:192-203.
27. Maximov I.I., Grinberg F., Shah N.J. Robust tensor estimation in diffusion tensor imaging. J. Magn. Reson. 2011, 213:136-144.
28. McGibney G., Smith M.R. An unbiased signal-to-noise ratio measure for magnetic resonance images. Med. Phys. 1993, 20:1077-1078.
29. Miller A.J., Joseph P.M. The use of power images to perform quantitative analysis on low SNR MR images. Magn. Reson. Imag. 1993, 11:1051-1056.
30. Mueller N., Jerschow A. Nuclear spin noise imaging. PNAS 2006, 103:6790-6792.
31. Nausner M., Schlagnitweit J., Smrecki V., Yang X., Jerschow A., Mueller N. Non-linearity and frequency shifts of nuclear magnetic spin-noise. J. Magn. Reson. 2009, 198:73-79.
32. Niendorf T., Dijkhuizen R.M., Norris D.G., van Lookeren Campagn M., Nicolay K. Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion-weighted imaging. Magn. Reson. Med. 1996, 36:847-857.
33. Nowak R.D. Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Process. 1999, 8:1408-1419.
34. Prah D.E., Paulson E.S., Nencka A.S., Schmainda K.M. A simple method for rectified noise floor suppression: phase-corrected real data reconstruction with application to diffusion-weighted imaging. Magn. Reson. Med. 2010, 64:418-429.
35. Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.T. Numerical Recipes, The Art of Scientific Computing 2007, Cambridge University Press. third ed.
36. Rice S.O. Mathematical analysis of random noise. Bell Syst. Tech. J. 1944, 23:282-444.
37. Rousseeuw P.J. Tutorial to robust statistics. J. Chemometrics 1991, 5:1-20.
38. Rousseeuw P.J., Croux C. Alternatives to the median absolute deviation. J. Am. Stat. Assoc. 1993, 88:1273-1356.
39. Rousseeuw P.J., Verboven S. Robust estimation in very small samples. Comp. Stat. Data Anal. 2002, 40:741-758.
40. Sijbers J., den Dekker A.J., Van Audekerke J., Verhoye M., Van Dyck D. Estimation of the noise in magnitude MR images. Magn. Reson. Imag. 1998, 16:87-90.
41. Sijbers J., den Dekker A.J. Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magn. Reson. Med. 2004, 51:586-594.
42. Sijbers J., Poot D., den Dekker A.J., Pintjens W. Automatic estimation of the noise variance from the histogram of a magnetic resonance image. Phys. Med. Biol. 2007, 52:1335-1348.
43. Smith S.M. Fast robust automated brain extraction. Human Brain Mapp. 2002, 17:143-155.
44. Thunberg P., Zetterberg P. Noise distribution in SENSE- and GRAPPA-reconstructed images: a computer simulation study. Magn. Reson. Imag. 2007, 25:1089-1094.
45. Verboven S., Hubert M. LIBRA: a MATLAB library for robust analysis. Chemomet. Intell. Lab. Syst. 2005, 75:127-136 .
46. Wirestam R., Bibic A., Laett J., Brockstedt S., Staahlberg F. Denoising of complex MRI data by wavelet-domain filtering: application to high-b-value diffusion-weighted imaging. Magn. Reson. Med. 2006, 56:1114-1120.