Convergence and Parameter Choice for Monte-Carlo Simulations of Diffusion MRI

IEEE Transactions on Medical Imaging

Volumn 28, Issue 9, 2009, Pages 1354-1364

  Hall, Matt G ^a   Alexander, Daniel C ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Data synthesis; diffusion magnetic resonance imaging (MRI); diffusion tensor imaging; Monte Carlo simulation; validation

Indexed keywords

ALGORITHM; ARTICLE; BRAIN EDEMA; BRAIN ISCHEMIA; COMPUTER SIMULATION; DIFFUSION WEIGHTED IMAGING; HUMAN; METHODOLOGY; MONTE CARLO METHOD; PATHOLOGY; REPRODUCIBILITY; STROKE;

ALGORITHMS; BRAIN EDEMA; BRAIN ISCHEMIA; COMPUTER SIMULATION; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; MONTE CARLO METHOD; REPRODUCIBILITY OF RESULTS; STROKE;

EID: 77951978178     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2009.2015756     Document Type: Article

References (40)

1. S. Mori, B. J. Crain, and P. J. Chackoand van Zijl, “PCM three dimensional tracking of axonal projections in the brain by magnetic resonance imaging,” Ann. Neurol, vol. 45, pp. 265–269, 1999.
2. M. Catani, R. J. Howard, S. Pajevic, and D. K. Jones, “Virtual in vivo interactive dissection of white matter fasciculi in the human brain,” Neuroimage, vol. 17, pp. 77–94, 2002.
3. J. Kucharczyk, J. Mintorovitch, H. Asgari, and M. Moseley, “Diffusion/perfusion MR imaging of acute cerebral ischemia,” Magn. Reson. Med., vol. 19, pp. 311–315, 1991.
4. Y. Assaf and P. J. Basser, “Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain,” NeuroImage, vol. 30, pp. 1100–1111, 2005.
5. D. C. Alexander, “A general framework for experimental design and its application in measuring direct tissue-microstructure features,” Magn. Reson. Med., vol. 60, pp. 439–448, 2008.
6. P. N. Sen and P. J. Basser, “A model for diffusion in white matter,” Biophys. J., vol. 89, pp. 2927–2938, 2005.
7. C.-L. Chin, F. W. Wehrli, S. N. Hwang, M. Takahashi, and D. B. Hackney, “Biexponential diffusion in the rat spinal cord: Computer simulations based on anatomical images of axonal architecture,” Magn. Reson. Med., vol. 47, pp. 455–460, 2002.
8. S. N. Hwang, C.-L. Chin, F. W. Wehrli, and D. B. Hackney, “An image-based difference model for simulating restricted diffusion,” Magn. Reson. Med., vol. 50, pp. 373–382, 2003.
9. J. C. Ford and D. B. Hackney, “Numerical model for calculation of apparent diffusion coefficients (ADC) in permeable cylinders—Comparison with measured ADC in rat spinal cord white matter,” Magn. Reson. Med., vol. 37, pp. 387–394, 1997.
10. C. Liu, R. Bammer, M. Acar, and M. E. Moseley, “Characterizing non-Gaussian diffusion by using generalized diffusiontensors,” Magn. Reson. Med., vol. 51, pp. 924–937, 2004.
11. W. S. Price, “Pulsed-field gradient nuclear magnetic resonance as a tool for studying translational diffusion: Part 1. Basic theory,” Concepts Magn. Reson., vol. 9, pp. 299–336, 1997.
12. D. C. Alexander and G. J. Barker, “Optimal imaging parameters for fibre-orientation estimation in diffusion MRI,” NeuroImage, vol. 27, pp. 357–367, 2005.
13. D. S. Tuch, T. G. Reese, M. R. Wiegell, and V. J. Wedeen, “Diffusion MRI of complex neural architecture,” Neuron, vol. 40, pp. 885–895, 2003.
14. J.-D. Tournier, F. Calamante, D. G. Gadian, and A. Connelly, “Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution,” NeuroImage, vol. 23, pp. 1176–1185, 2004.
15. D. C. Alexander, “Multiple fibre reconstructions for diffusion MRI,” Ann. NY Acad. Sci., vol. 1046, pp. 113–133, 2005.
16. C. H. Neuman, “Spin echo of spins diffusing in a bounded medium,” J. Chem. Phys., vol. 60, pp. 4508–4511, 1974.
17. P. T. Callaghan, “Pulsed-gradient spin-echo NMR for planar, cylindrical and spherical pores under conditions of wall relaxation,” Magn. Reson. Med., vol. 113, pp. 53–59, 1995.
18. O. Soderman and B. Jonsson, “Restricted diffusion in cylindrical geometry,” J. Magn. Reson. A, vol. 117, pp. 94–97, 1995.
19. A. Szafer, J. Zhong, and J. C. Gore, “Theoretical model for water diffusion in tissues,” Magn. Reson. Med., vol. 33, pp. 697–712, 1995.
20. G. J. Stanisz, “Diffusion MR in biological systems: Tissue compartments and exchange,” Israel J. Chem., vol. 43, pp. 33–44, 2003.
21. 2 and compartmentation in frog sciatic nerve,” Magn. Reson. Med., vol. 42, pp. 911–918, 1999.
22. L. L. Latour, K. Svoboda, P. P. Mitra, and C. H. Sotak, “Time-dependent diffusion of water in a biological model system,” Proc. Nat. Acad. Sci., vol. 91, pp. 1229–1233, 1994.
23. G. J. Stanisz, A. Szafer, G. A. Wright, and M. Henkelman, “An analytical model of restricted diffusion in bovine optic nerve,” Magn. Reson. Med., vol. 37, pp. 103–111, 1997.
24. A. Duh, A. Mohoric, and J. Stepisnik, “Computer simulation of the spin-echo spatial distribution in the case of restricted self-diffusion,” J. Magn. Reson., vol. 148, pp. 257–266, 2001.
25. D. G. Regan and P. W. Kuchel, “Simulations of NMR-detected diffusion in suspension of red cells; The signatures of q-space plots of various lattice arrangements,” Eur. Biophys. J., vol. 31, pp. 563–574, 2003.
26. H.-G. Lipinski, “Monte-Carlo simulation of extracellular diffusion in brain tissues,” Phys. Med. Biol., vol. 35, pp. 441–447, 1990.
27. O. Kallenberg, Foundations of Modern Probability. New York: Springer-Verlag, 1997.
28. T. J. Reese, O. Heid, R. M. Weisskoff, and V. J. Wedeen, “Reduction of eddy-current-induced distortion using a twice refocused spin-echo,” Magn. Reson. Med., vol. 49, pp. 177–182, 2003.
29. J. Frahm, K. D. Merboldt, W. Haenicke, and A. Haase, “Stimulated echo imaging,” J. Magn. Reson., vol. 64, no. 1, pp. 81–93, 1985.
30. J. S. Murday and R. M. Cotts, “Self-diffusion coefficient of liquid lithium,” J. Chem. Phys., vol. 48, pp. 4938–4945, 1984.
31. B. Balinov, Jonsson, P. Linse, and Soderman, “The NMR self-diffusion method applied to restricted diffusion. Simulation of echo attenuation from molecules in spheres and between planes,” J. Magn. Reson. Series A, vol. 104, pp. 17–25, 1993.
32. C. H. Sotak, “The role of diffusion tensor imaging in the evaluation of ischemic brain injury—A review,” NMR Biomed., vol. 15, pp. 561–569, 2002.
33. D. G. Norris, T. Niendorf, and D. Leibfritz, “A theory of diffusion contrast in healthy infarcted tissue,” in Proc. SMRM 12th Annu. Meeting, 1993, vol. 579.
34. E. Sykova, J. Svoboda, J. Polakand, and A. Chvatal, “Extracellular volume fraction and diffusion characteristics during progressive ischemia and terminal anoxia in the spinal cord of the rat,” J. Cereb Blood Flow, vol. 14, pp. 301–311, 1994.
35. A. W. Anderson, J. Zhong, O. A. Petroff, A. Szafer, B. R. Ransom, J. W. Pritchard, and J. C. Gore, “Effects of osmotically driven cell volume changes on diffusion-weighted imaging of the rat optic nerve,” Magn. Reson. Med., vol. 35, pp. 162–167, 1996.
36. F. Aboitiz, A. B. Scheibel, R. S. Fisher, and E. Zaidel, “Fiber composition of the human corpus callosum,” Brain Res., vol. 598, pp. 143–153, 1992.
37. D. Ben-Avraham and S. Havlin, Diffusion and Reactions in Fractals and Disordered Systems. Cambridge, U.K.: Cambridge Univ. Press, 2000.
38. D. Kirk, Ed., Graphics Gems III. San Diego, CA: AP Professional, 1994.
39. M. Slater, Y. Chrysanthou, and A. Steed, Computer Graphics and Virtual Environments: From Realism to Real Time. Reading, MA: Addison Wesley, 2001.
40. D. LeBihan, S.-I. Urayama, T. Aso, T. Hanakawa, and H. Fukuyama, “Direct and fast detection of activation in the human brain with diffusion MRI,” Proc. Nat. Acad. Sci., vol. 103, no. 21, pp. 8263–8268, 2006.