Estimation of diffusion properties in crossing fiber bundles

IEEE Transactions on Medical Imaging

Volumn 29, Issue 8, 2010, Pages 1504-1515

  Caan, Matthan W A ^a,b   Khedoe, H Ganesh ^a   Poot, Dirk H J ^c   Den Dekker, Arjan J ^a   Olabarriaga, Silvia D ^d   Grimbergen, Kees A ^d   Van Vliet, Lucas J ^a   Vos, Frans M ^a,b  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b ACADEMIC MEDICAL CENTER   (Netherlands)

^c UNIVERSITY OF ANTWERP   (Belgium)

^d UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

Cramr Rao analysis; diffusion properties; diffusion weighted magnetic resonance imaging; dual tensor model; Monte Carlo simulations

Indexed keywords

AXIAL DIFFUSION; COMPARATIVE STUDIES; DIFFUSION PROPERTIES; DIFFUSION WEIGHTING; DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING; DIFFUSIVITIES; ESTIMATING PARAMETERS; FIBER BUNDLES; FRACTIONAL ANISOTROPY; FUNDAMENTAL LIMITS; HETEROGENEOUS TISSUES; IN-FIBER; LEVENBERG-MARQUARDT OPTIMIZATION; LOWER BOUNDS; MAXIMUM LIKELIHOOD FUNCTION; MODEL PARAMETERS; MONTE CARLO SIMULATION; NOISE MODELS; OPTIMIZATION FRAMEWORK; ORIENTATION PARAMETER; SINGLE PARAMETER; TENSOR MODEL; WHITE MATTER;

COSMIC RAY DETECTORS; DIFFUSION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MAXIMUM LIKELIHOOD ESTIMATION; MONTE CARLO METHODS; OPTIMIZATION; PARAMETER ESTIMATION; TENSORS;

COMPUTER SIMULATION;

ARTICLE; BIOLOGICAL MODEL; BRAIN; COMPUTER SIMULATION; DIFFUSION TENSOR IMAGING; HUMAN; METHODOLOGY; MONTE CARLO METHOD; NERVE FIBER; NORMAL DISTRIBUTION; PHYSIOLOGY; REPRODUCIBILITY; SIGNAL PROCESSING; STATISTICAL MODEL;

BRAIN; COMPUTER SIMULATION; DIFFUSION TENSOR IMAGING; HUMANS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; MONTE CARLO METHOD; NERVE FIBERS; NORMAL DISTRIBUTION; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77955323071     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2049577     Document Type: Article

References (54)

1. P. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, "In vivo fiber tractography using DT-MRI data, " Magn. Reson. Med., vol.44, pp. 625-632, 2000.
2. D. S. Tuch et al., "High angular resolution diffusion imaging of the human brain, " in Proc. 8th ISMRM, Philadelphia, PA, 1999, p. 321.
3. S.-K. Song, S.-W. Sun, W.-K. Ju, S.-J. Lin, A. H. Cross, and A. H. Neufeld, "Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia, " Neu-roImage, vol.20, pp. 1714-1722, 2003.
4. M. D. Budde, M. Xie, A. H. Cross, and S. K. Song, "Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: A quantitative pixelwise analysis, " J. Neurosci., vol.29, pp. 2805-2813, 2009.
5. T. Hosey, G. Williams, and R.Ansorge, "Inferenceof multiplefiber orientations in high angular resolution diffusion imaging, " Magn. Reson. Med., vol.54, pp. 1480-1489, 2005.
6. M. Caan, K. Vermeer, L. van Vliet, C. Majoie, B. Peters, G. den Heeten, and F. Vos, "Shaving diffusion tensor images in discriminant analysis: A study into schizophrenia, " Med. Image Anal., vol.10, pp. 841-849, 2006.
7. C. Sage, R. Peeters, A. Gorner, W. Robberecht, and S. Sunaert, "Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis, " Neu-roimage, vol.34, pp. 486-499, 2007.
8. D. Alexander and G. Barker, "Optimal imaging parameters for fiber-orientation estimation in diffusion MRI, " Neuroimage, vol.27, no.2, pp. 357-367, 2005.
9. L. R. Frank, "Anisotropy in high angular resolution diffusion-weighted MRI, " Magn. Reson. Med., vol.45, pp. 935-939, 2001.
10. B. Kreher et al., "Multitensor approach for analysis and tracking of complex fiber configurations, " Magn. Res. Med., vol.54, pp. 1216-1225, 2005.
11. S. Peled et al., "Geometrically constrained two-tensor model for crossing tracts in DWI, " Magn Reson Med., vol.24, pp. 1263-1270, 2006.
12. E. Özarslan and T. H. Mareci, "Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging, " Magn. Reson. Med., vol.50, pp. 955-965, 2003.
13. J. D. Tournier, F. Calamante, and A. Connelly, "Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution, " Neuroimage, vol.35, no.4, pp. 1459-1472, 2007.
14. D. Tuch, "Diffusion MRI of complex tissue structure, " Ph.D. dissertation, MIT, Cambridge, 2002.
15. A. A. Qazi, A. Radmanesh, L. O'Donnell, G. Kindlmann, S. Peled, S. Whalen, C. F. Westin, and A. J. Golby, "Resolving crossings in the corticospinal tract by two-tensor streamline tractography: Method and clinical assessment using fMRI, " Neuroimage, 2008.
16. J. Malcolm, M. Shenton, and Y. Rathi, "Two-tensor tractography using a constrained filter, " in Proc. Med. Image Computing Computer Assist. Intervent. (MICCAI), 2009, pp. 894-902.
17. J. Malcolm, M. Shenton, and Y. Rathi, "Neural tractography using an unscented kalman filter, " in Inf. Process. Med. Imag. (IPMI), 2009, pp. 126-138.
18. M. Descoteaux, R. Deriche, T. Knosche, and A.Anwander, "Deterministic and probabilistic tractography based on complex fibre orientation distributions, " IEEE Trans. Med Imag., vol.28, no.2, pp. 269-286, Feb. 2009.
19. A. J. den Dekker and J. Sijbers, "Estimation of signal and noise parameters from MR data, " in Advanced Image Processing in Magnetic Resonance Imaging. Boca Raton, FL: CRC, 2005, vol.27, Signal Process. Commun., ch. 4, pp. 85-143.
20. J. L. Andersson, "Maximum a posteriori estimation of diffusion tensor parameters using a Rician noise model: Why, how and but, " Neuroimage, vol.42, pp. 1340-1356, 2008.
21. B. Landman, P. L. Bazin, and J. Prince, "Diffusion tensor estimation by maximizing Rician likelihood, " in Proc. IEEE 11th ICCV, 2007, pp. 2433-2440
22. P. Fillard, X. Pennec, V. Arsigny, and N. Ayache, "Clinical DT-MRI estimation, smoothing, and fiber tracking with log-euclidean metrics, " IEEE Trans. Med. Imag., vol.26, no.11, pp. 1472-1482, Nov. 2007.
23. S. Basu, T. Fletcher, and R. Whitaker, "Rician noise removal in diffusion tensor MRI, " in Proc. Miccai '06, 2006, vol.4190, LNCS, pp. 117-125.
24. S. Aja-Fernandez, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin, "Restoration of DWI data using a Rician LMMSE estimator, " IEEE Trans. Med. Imag., vol.27, no.10, pp. 1389-1403, Oct. 2008.
25. C. G. Koay, L. C. Chang, C. Pierpaoli, and P. J. Basser, "Error propagation framework for diffusion tensor imaging via diffusion tensor representations, " IEEE Trans. Med. Imag., vol.26, no.8, pp. 1017-1034, Aug. 2007.
26. D. LeBihan, E. Breton, D. Lallemand, P. Grenier, E. Cabanis, and M. Laval-Jeantet, "MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders, " Radiology, vol.161, pp. 401-407, 1986.
27. C. Pierpaoli et al., "Diffusion tensor MR imaging of the human brain, " Radiology, vol.201, pp. 637-648, 1996.
28. D. Jones, M. Horsfield, and A. Simmons, "Optimal strategies for measuring diffusion in anisotropic systems by Magnetic Resonance Imaging, "Magn. Reson. Med, vol.42, pp. 515-525, 1999.
29. Y. Bito, S. Hirata, and E. Yamamoto, "Optimal gradient factors for ADC measurements, " in Proc. ISMRM, 1995, p. 913.
30. S. Peled, "New perspectives on the sources of white matter DTI signal, " IEEE Trans. Med. Imag., vol.26, no.11, pp. 1448-1455, Nov. 2007.
31. Y. Assaf and P. J. Basser, "Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain, " Neu-roImage, vol.27, pp. 48-58, 2005.
32. P. Kingsley and W. Monahan, "Selection of the optimum b-factor for diffusion-weighted magnetic resonance imaging assessment of ischemic stroke, " Magn. Reson. Med, vol.51, pp. 996-1001, 2004.
33. T. Behrens, H. Berg, S. Jbabdi, M. Rushworth, and M. Woolricha, "Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?, " NeuroImage, vol.34, pp. 144-155, 2007.
34. Y. Assaf, D. Ben-Bashat, J. Chapman, S. Peled, I. Biton, M. Kafri, Y. Segev, T. Hendler, A. Korczyn, M. Graif, and Y. Cohen, "High b-value q-space analyzed diffusion-weighted MRI: Application to Multiple Sclerosis, " Magn. Reson. Med., vol.47, pp. 115-126, 2002. (Pubitemid 34019700)
35. D. K. Jones and P. J. Basser, "Squashing peanuts and smashing pumpkins: How noise distorts diffusion-weighted MR data, " Magn. Reson. Med, vol.52, no.5, pp. 979-993, 2004.
36. O. Brihuega-Moreno, F. P. Heese, and L. D. Hall, "Optimization of diffusion measurements using cramer-rao lower bound theory and its application to articular cartilage, " Magn. Reson. Med., vol.50, no.5, pp. 1069-1076, 2003.
37. D. C. Alexander, "A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features, " Magn. Reson. Med., vol.60, no.2, pp. 439-448, 2008.
38. P. Basser and C. Pierpaoli, "Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI, " J. Magn. Reson. B, vol.111, pp. 209-219, 1996.
39. H. Gudbjartsson and S. Patz, "The Rician distribution of noise MRI data, " Magn. Res. Red., pp. 910-914, 1995.
40. A. van den Bos, Parameter Estimation for Scientists and Engineers. Hoboken, NJ: Wiley., 2007.
41. A. J. denDekker, S. vanAert, A. van den Bos, andD. vanDyck, "Maximum likelihood estimation ofstructure parameters from high resolution electron microscopy images. Part I: A theoretical framework, " Ul-tramicroscopy, vol.104, pp. 83-106, 2005. (Pubitemid 40848631)
42. J. Sijbers and A. J. den Dekker, "Maximum likelihood estimation of signal amplitude and noise variance from MR data, " Magn. Reson. Med, vol.51, pp. 586-594, 2004.
43. O. T. Karlsen, R. Verhagen, and W. Bovee, "Parameter estimation from Rician-distributed data sets using a maximum likelihood estimator: Application to T-1 and perfusion measurements, " Magn. Reson. Med., vol.41, no.3, pp. 614-623, 1999.
44. C. Pierpaoli and D. K. Jones, "Removing CSF contamination in brain DT-MRIs by using a two-compartment tensor model, " in Proc. ISMRM, 2005, p. 1215.
45. J. D. Carew, C. G. Koay, G. Wahba, A. L. Alexander, M. E. Meyerand, and P. J. Basser, The asymptotic behavior of the nonlinear estimators of the diffusion tensor and tensor-derived quantities with implications for group analysis Univ. Wisconsin, Tech. Rep. 1132, 2006, Tech. Rep. 1132.
46. J. D. Tournier, C. H. Yeh, F. Calamante, K. H. Cho, A. Connelly, and C. P. Lin, "Resolving crossing fibres using constrained spherical decon-volution: Validation using diffusion-weighted imaging phantom data, " Neuroimage, vol.42, no.2, pp. 617-625, 2008.
47. J.-F. Mangin, C. Poupon, C. Clark, D. L. Bihan, and I. Bloch, "Eddy-current distortion correction and robust tensor estimation for MR diffusion imaging, " in Proc. MICCAI'01, 2006, pp. 186-193.
48. M. Caan et al., "Adaptive noise filtering for accurate and precise diffusion estimation in fiber crossings, " in Proc. MICCAI, 2010.
49. S. Olabarriaga, T. Glatard, K. Boulebiar, and P. T. de Boer, "From 'low-hanging' to 'user-ready': Initial steps into a healthgrid, " in HealthGrid. Chicago, IL: IOS Press, 2008, Studies Health Technol. Informatics, pp. 70-79.
50. N. Toussaint, A. van Muiswinkel, F. G. Hoogenraad, R. Holthuizen, and S. Sunaert, "Resolving fiber crossings: A two fiber model simulation result, " in Proc. ISMRM, 2005, vol.13, p. 1339.
51. V. Prckovska, A. F. Roebroeck, W. Pullens, A. Vilanova, and B. M. T. Romeny, "Optimal acquisition schemes in high angular resolution diffusion weighted imaging, " in Proc. MICCAI, 2008, vol.5242, pp. 9-17.
52. O. Pasternak, Y. Assaf, N. Intrator, and N. Sochen, "Variational multiple-tensor fitting of fiber-ambiguous diffusion-weighted magnetic resonance imaging voxels, " Magn. Reson. Imag., vol.26, pp. 1133-1144, 2008.
53. H. E. Assemlal, D. Tschumperle, and L. Brun, "Efficient and robust computation of PDF features from diffusion mr signal, " Med. Image Anal., vol.13, no.5, pp. 715-729, 2009.
54. T. P. Hosey, S. G. Harding, T. A. Carpenter, R. E. Ansorge, and G. B. Williams, "Application of a probabilistic double-fibre structure model to diffusion-weighted MR images of the human brain, " Magn. Reson. Imag., vol.26, pp. 236-245, 2008.