Gibbs ringing in diffusion MRI

Magnetic Resonance in Medicine

Volumn 76, Issue 1, 2016, Pages 301-314

  Veraart, Jelle ^a,b   Fieremans, Els ^a   Jelescu, Ileana O ^a   Knoll, Florian ^a   Novikov, Dmitry S ^a  

^a NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

diffusion kurtosis imaging; diffusion tensor imaging; extrapolation; total variation; truncation artifact

Indexed keywords

ARTIFACT; DIFFUSION TENSOR IMAGING; FILTRATION; PUNISHMENT; QUANTITATIVE STUDY;

EID: 84974822398     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.25866     Document Type: Article

References (61)

1. Wilbraham H. On a certain periodic function. Camb Dubl Math J 1848;3:198–201.
2. Gibbs JW. Fourier's series. Nature 1898;59:200.
3. Liang ZP, Boada F, Constable RT, Haacke E, Lauterbur P, Smith M. Constrained reconstruction methods in MR imaging. Rev Magn Reson Med 1992;4:67–186.
4. Liang ZP, Lauterbur PC. Principles of magnetic resonance imaging. New York, NY, USA: IEEE Press Series on Biomedical Engineering; 2000.
5. Yablonskiy DA, Sukstanskii AL, Luo J, Wang X. Voxel spread function method for correction of magnetic field inhomogeneity effects in quantitative gradient-echo-based MRI. Magn Reson Med 2013;70:1283–1292.
6. Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 2011;65:823–836.
7. Veraart J, Van Hecke W, Sijbers J. Constrained maximum likelihood estimation of the diffusion kurtosis tensor using a Rician noise model. Magn Reson Med 2011;66:678–686.
8. Barker G, Parker G, Wheeler-Kingshott C. Gibbs ringing and negative ADC values. In Proceedings of the 9th meeting of the International Society for Magnetic Resonance in Medicine. Glasgow (UK). 2001. pp 1546.
9. Tournier JD, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med 2011;65:1532–1556.
10. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010;23:803–820.
11. Glenn GR, Tabesh A, Jensen JH. A simple noise correction scheme for diffusional kurtosis imaging. Magn Reson Imaging 2015;33:124–133.
12. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432–1440.
13. Pfefferbaum A, Sullivan EV. Increased brain white matter diffusivity in normal adult aging: relationship to anisotropy and partial voluming. Magn Reson Med 2003;49:953–961.
14. Hirsch JG, Bock M, Essig M, Schad LR. Comparison of diffusion anisotropy measurements in combination with the flair-technique. Magn Reson Imaging 1999;17:705–716.
15. Falconer JC, Narayana PA. Cerebrospinal fluid-suppressed high-resolution diffusion imaging of human brain. Magn Reson Med 1997;37:119–123.
16. Yang AW, Jensen JH, Hu CC, Tabesh A, Falangola MF, Helpern JA. Effect of cerebral spinal fluid suppression for diffusional kurtosis imaging. J Magn Reson Imaging 2013;37:365–371.
17. Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DT-MRI data. Neuroimage 2005;26:546–554.
18. Vos SB, Jones DK, Viergever MA, Leemans A. Partial volume effect as a hidden covariate in DTI analyses. Neuroimage 2011;55:1566–1576.
19. Jelescu IO, Veraart J, Adisetiyo V, Milla SS, Novikov DS, Fieremans E. One diffusion acquisition and different white matter models: how does microstructure change in human early development based on WMTI and NODDI? NeuroImage 2015;107:242–256.
20. Ghosh A, Milne T, Deriche R. Constrained diffusion kurtosis imaging using ternary quartics and mle. Magn Reson Med 2014;71:1581–1591.
21. Koay CG, Chang LC, Carew JD, Pierpaoli C, Basser PJ. A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J Magn Reson 2006;182:115–125.
22. Constable R, Henkelman R. Data extrapolation for truncation artifact removal. Magn Reson Med 1991;17:108–118.
23. Tsao J. Extension of finite-support extrapolation using the generalized series model for MR spectroscopic imaging. IEEE Trans Med Imaging, 2001;20:1178–1183.
24. Amartur S, Haacke EM. Modified iterative model based on data extrapolation method to reduce Gibbs ringing. J Magn Reson Imag 1991;1:307–317.
25. Amartur S, Liang ZP, Boada F, Haacke EM. Phase-constrained data extrapolation method for reduction of truncation artifacts. J Magn Reson Imaging 1991;1:721–724.
26. Block KT, Uecker M, Frahm J. Suppression of MRI truncation artifacts using total variation constrained data extrapolation. Int J Biomed Imag 2008;2008:184123.
27. Perrone D, Aelterman J, Kudzinava M, Sijbers J, Pizurica A, Philips W, Leemans A. Correction of Gibbs ringing in diffusion MRI data using total variation regularization. In Fourth annual meeting of the ISMRM Benelux Chapter. Leuven (Belgium). 2012; 99–99.
28. Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D 1992;60:259–268.
29. Dobson DC, Santosa F. Recovery of blocky images from noisy and blurred data. SIAM J Appl Math 1996;56:1181–1198.
30. Bredies K, Kunisch K, Pock T. Total generalized variation. SIAM J Imaging Sci 2010;3:492–526.
31. Knoll F, Bredies K, Pock T, Stollberger R. Second order total generalized variation (TGV) for MRI. Magn Reson Med 2011;65:480–491.
32. Knoll F, Schultz G, Bredies K, Gallichan D, Zaitsev M, Hennig J, Stollberger R. Reconstruction of undersampled radial patloc imaging using total generalized variation. Magn Reson Med 2013;70:40–52.
33. Feng W, Lei H. Single-image super-resolution with total generalised variation and Shearlet regularisations. IET Image Process 2014;8:833–845.
34. Gnahm C, Nagel AM. Anatomically weighted second-order total variation reconstruction of 23 Na MRI using prior information from 1H MRI. NeuroImage 2015;105:452–461.
35. Hu Y, Jacob M. Higher degree total variation (HDTV) regularization for image recovery. IEEE Trans Image Process 2012;21:2559–2571.
36. Martín A, Schiavi E. Automatic total generalized variation-based DTI Rician denoising. In Image Analysis and Recognition. Springer; Heidelberg (Germany). 2013. pp 581–588.
37. Xu J, Feng X, Hao Y, Han Y. Image decomposition using adaptive second-order total generalized variation. Signal Image Video Process 2014;8:39–47.
38. Chambolle A, Pock TA. first-order primal-dual algorithm for convex problems with applications to imaging. J Math Imaging Vis 2011;40:120–145.
39. Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006;52:1289–1306.
40. Candès EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006;52:489–509.
41. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182–1195.
42. Morozov V. On the discrepancy principle for solving operator equations by the method of regularization. Zh Vy¯chisl Mat Mat Fiz 1968;8:295–309.
43. Scherzer O. The use of Morozov's discrepancy principle for Tikhonov regularization for solving nonlinear ill-posed problems. Computing 1993;51:45–60.
44. Coupé P, Manjón JV, Gedamu E, Arnold D, Robles M, Collins DL. Robust rician noise estimation for MR images. Med Image Anal 2010;14:483–493.
45. Kiselev VG. The cumulant expansion: an overarching mathematical framework for understanding diffusion NMR. Diffusion MRI: Theory, Methods, and Applications; Oxford University Press, Oxford (UK). 2011. pp 152–168.
46. Mattiello J, Basser PJ, Le Bihan D. The b matrix in diffusion tensor echo-planar imaging. Magn Reson Med 1997;37:292–300.
47. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259–267.
48. Veraart J, Sijbers J, Sunaert S, Leemans A, Jeurissen B. Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. NeuroImage 2013;81:335–346.
49. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson Ser B 1996;111:209–219.
50. Lu H, Jensen JH, Ramani A, Helpern JA. Three-dimensional characterization of non-gaussian water diffusion in humans using diffusion kurtosis imaging. NMR Biomed 2006;19:236–247.
51. Kiselev VG, Il'yasov KA. Is the “biexponential diffusion” biexponential?. Magn Reson Med 2007;57:464–469.
52. Uğurbil K, Xu, J, Auerbach EJ, et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. Neuroimage 2013;80:80–104.
53. Sotiropoulos SN, Jbabdi, S, Xu J, et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 2013;80:125–143.
54. Veraart J, Rajan J, Peeters RR, Leemans A, Sunaert S, Sijbers J. Comprehensive framework for accurate diffusion MRI parameter estimation. Magn Reson Med 2013;70:972–984.
55. Veraart J, Poot DHJ, Van Hecke W, Blockx I, Van der Linden A, Verhoye M, Sijbers J. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magn Reson Med 2011;65:138–145.
56. Freiberger M, Knoll F, Bredies K, Scharfetter H, Stollberger R. The agile library for biomedical image reconstruction using GPU acceleration. Comput Sci Eng 2013;15:34–44.
57. Veraart J, Fieremans E, Novikov DS. Noise map estimation in diffusion MRI using Random Matrix Theory. In Proceedings of the 23th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Toronto, Canada, 2015. p. 1023.
58. Candes E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Probl 2007;23:969.
59. Jones DK, Basser PJ. “Squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted MR data. Magn Reson Med 2004;52:979–993.
60. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med 1995;34:910–914.
61. Koay CG, Basser PJ. Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. J Magn Reson 2006;179:317–322.