A note on the validity of statistical bootstrapping for estimating the uncertainty of tensor parameters in diffusion tensor images

IEEE Transactions on Medical Imaging

Volumn 27, Issue 10, 2008, Pages 1506-1514

  Yuan, Ying ^a   Zhu, Hongtu ^b   Ibrahim, Joseph G ^b   Lin, Weili ^c   Peterson, Bradley S ^d  

^a The University of North Carolina at Chapel Hill   (United States)

^b University of North Carolina at Chapel Hill   (United States)

^c UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^d NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

Author keywords

Acquisition scheme; Bootstrap; Diffusion tensor; Fiber tracks; Principal direction

Indexed keywords

CRYSTALLOGRAPHY; DIFFUSION; EIGENVALUES AND EIGENFUNCTIONS; ESTIMATION; FIBER OPTICS; IMAGE ENHANCEMENT; IMAGING TECHNIQUES; MAGNETIC RESONANCE IMAGING; PARAMETER ESTIMATION; RESONANCE; SEMICONDUCTOR DOPING; STATISTICAL METHODS; UNCERTAINTY ANALYSIS;

ACQUISITION SCHEME; BOOTSTRAP; BOOTSTRAP METHODS; DIFFUSION TENSOR; DIFFUSION TENSORS; DIFFUSION WEIGHTED IMAGES; DIFFUSION-WEIGHTED IMAGING; EIGENVALUES; EIGENVALUES AND EIGENVECTORS; EIGENVECTORS; FIBER TRACKS; FRACTIONAL ANISOTROPY; IN-DIFFUSION; PRINCIPAL DIRECTION; SIMULATION STUDIES; STATISTICAL PARAMETERS; WILD BOOTSTRAPS;

TENSORS;

ALGORITHM; ARTICLE; ARTIFACT; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DIFFUSION WEIGHTED IMAGING; EVALUATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; VALIDATION STUDY;

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

EID: 52649088891     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2008.926069     Document Type: Article

References (32)

1. C. Pierpaoli and P. J. Basser, "Toward a quantitative assessment of diffusion anisotropy," Magn. Reson. Med., vol. 36, pp. 893-906, 1996.
2. P. J. Basser and S. Pajevic, "Dealing with uncertainty in diffusion tensor MR data," Israel J. Chem., vol. 43, pp. 129-144, 2003.
3. P. J. Basser and S. Pajevic, "Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise," Magn. Reson. Med., vol. 44, pp. 41-50, 2000.
4. T. E. J. Behrens, M. W. Woolrich, M. Jenkinson, H. Johansen-Berg, R. G. Nunes, S. Clare, P. M. Matthews, J. M. Brady, and S. M. Smith, "Characterization and propagation of uncertainty in diffusion-weighted imaging," Magn. Reson. Med., vol. 50, pp. 1077-1088, 2003.
5. A. W. Anderson, "Theoretical analysis of the effects of noise on diffusion tensor imaging," Magn. Reson. Med, vol. 46, pp. 1174-1188, 2001.
6. H. T. Zhu, H. P. Zhang, J. G. Ibrahim, and B. Peterson, "Statistical analysis of diffusion tensors in diffusion-weighted magnetic resonance imaging Data (with discussion)," J. Amer. Stat. Assoc., vol. 102, pp. 1085-1102, 2007.
7. S. Pajevic and P. J. Basser, "Parametric and non-parametric statistical analysis of DT-MRI Data," J. Magn. Reson., vol. 161, pp. 1-14, 2003.
8. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. London, U.K.: Chapman and Hall, 1993.
9. B. Whitcher, D. S. Tuch, J. J. Wisco, A. G. Sorenson, and L. Wang, "Using the wild Bootstrap to quantify uncertainty in DTI," Human Brain Mapp., vol. 29, pp. 346-362, 2008.
10. S. Chung, Y. Liu, and R. G. Henry, "Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters," NeuroImage, vol. 33, pp. 531-541, 2006.
11. M. Lazar and A. L. Alexander, "Bootstrap white matter tractography (BOOT-TRAC)," NeuroImage, vol. 24, pp. 524-532, 2005.
12. R. L. O'Gorman and D. K. Jones, "Just how much data need to be collected for reliable bootstrap DT-MRI?," Magn. Reson. Med., vol. 56, pp. 884-890, 2006.
13. E. O. Stejskal and J. E. Tanner, "Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient," J. Chem. Phys., vol. 42, pp. 288-292, 1965.
14. P. B. Kingsley, "Introduction to diffusion tensor imaging mathematics: Part II. Anisotropy, diffusion-weighting factors, and gradient encoding schemes," Concepts Magn. Reson., vol. 28A, pt. A, pp. 123-154, 2006.
15. P. J. Basser, J. Mattiello, and D. LeBihan, "Estimation of the effective self-diffusion tensor from the NMR spin echo," J. Magn. Reson., ser. B, vol. 103, pp. 247-254, 1994.
16. R. Salvador, A. Pena, D. K. Menon, T. A. Carpenter, J. D. Pickard, and E. T. Bullmore, "Formal characterization and extension of the linearized diffusion tensor model," Human Brain Mapp., vol. 24, pp. 144-155, 2005.
17. T. E. Conturo, N. F. Lori, T. S. Cull, E. Akbudak, A. Snyder, J. S. Shimony, R. C. McKinstry, H. Burton, and M. E. Raichte, "Tracking neuronal fiber pathways in the living human brain," Proc. Nat. Acad. Sci. USA, vol. 96, pp. 10422-10427, 1999.
18. D. Xu, S. Mori, M. Solaiyappan, P. C. van Zijl, and C. Davatzikos, "A framework for callosal fiber distribution analysis," NeuroImage, vol. 17, pp. 1131-1143, 2002.
19. R. Y. Liu, "Bootstrap procedure under some non-i.i.d. models," Ann. Stat., vol. 16, pp. 1696-1708, 1988.
20. E. Mammen, "Bootstrap and wild bootstrap for high, dimensional linear models," Ann. Stat., vol. 21, pp. 255-285, 1993.
21. E. Flachaire, "Bootstrapping heteroscedastic regression models: Wild bootstrap vs. pairs bootstrap," Computat. Stat. Data Anal., vol. 49, pp. 361-376, 2005.
22. H. T. Zhu, J. G. Ibrahim, N. S. Tang, D. B. Rowe, X. J. Hao, R. Bansal, and B. Peterson, "A statistical analysis of brain morphology using wild bootstrapping," IEEE Trans. Med Imag., vol. 26, no. 7, pp. 954-966, Jul. 2007.
23. A. W. van der Vaart and J. Wellner, Weak Convergence and Empirical Processes. New York: Springer, 1996.
24. M. L. Eaton and D. E. Tyler, "On Wielandt's inequality and its application to the asymptotic distribution of the eigenvalues of a random symmetric matrix," Ann. Stat., vol. 19, pp. 260-271, 1991.
25. H. Zhu, D. Xu, R. Amir, X. Hao, H. Zhang, K. Alayar, B. Ravi, and B. Peterson, "A statistical framework for the classification of tensor morphologies in diffusion tensor images," Magn. Reson. Imag., vol. 24, pp. 569-582, 2006.
26. D. C. Alexander, G. J. Barker, and S. R. Arridge, "Detection and modelling of non-gaussian apparent diffusion coefficient profiles in human brain data," Magn. Reson. Med., vol. 48, pp. 331-340, 2002.
27. L. R. Frank, "Characterization of anisotropy in high angular resolution diffusion-weighted MRI," Magn. Reson. Med., vol. 47, pp. 1083-1099, 2002.
28. D. S. Tuch, T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen, "High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity," Magn. Reson. Med., vol. 48, pp. 577-582, 2002.
29. V. J. Wedeen, P. Hagmann, W. I. Tseng, T. G. Reese, and R. M. Weisskoff, "Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging," Magn. Reson. Med., vol. 54, pp. 1377-1386, 2005.
30. H. White, Estimation, Inference, and Specification Analysis. Cambridge, U.K.: Cambridge Univ. Press, 1994.
31. C. PiChiroerpaoli, P. Jezzard, P. J. Basser, A. Barnett, and G. D. Chiro, "Diffusion tensor MR imaging of the human brain," Radiology, vol. 201, pp. 637-648, 1996.
32. L. R. Frank, "Anisotropy in high angular resolution diffusion weighted MRI," Magn. Reson. Med., vol. 45, pp. 935-939, 2001.