No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Gray matter parcellation constrained full brain fiber bundling with diffusion tensor imaging
2. D. Le Bihan, J. F. Mangin, C. Poupon, C. A. Clark, S. Pappata, N. Molko, and H. Chabriat, “Diffusion tensor imaging: Concepts and applications,” J. Magn. Reson. Imaging 13, 534–546 (2001).
3. V. J. Schmithorst, M. Wilke, B. J. Dardzinski, and S. K. Holland, “Cognitive functions correlate with white matter architecture in a normal pediatric population: A diffusion tensor MRI study,” Hum Brain Mapp 26(2), 139–47 (2005).
4. V. L. Morgan, A. Mishra, A. T. Newton, J. C. Gore, and Z. Ding, “Integrating functional and diffusion magnetic resonance imaging for analysis of structure-function relationship in the human language network,” PLoS ONE 4(8), e6660 (2009).
5. M. Filippi, M. Cercignani, M. Inglese, M. A. Horsfield, and G. Comi, “Diffusion tensor magnetic resonance imaging in multiple sclerosis,” Neurology 56(3), 304–311 (2001).
6. C. Pierpaoli, A. Barnett, S. Pajevic, R. Chen, L. R. Penix, A. Virta, and P. Basser, “Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture,” NeuroImage 13, 1174–1185 (2001).
7. B. A. Assaf, F. B. Mohamed, K. J. Abou-Khaled, J. M. Williams, M. S. Yazeji, J. Haselgrove, and S. H. Faro, “Diffusion tensor imaging of the hippocampal formation in temporal lobe epilepsy,” AJNR Am. J. Neuroradiol. 24(9), 1857–1862 (2003).
8. J. R. Wozniak, B. A. Mueller, P. N. Chang, R. L. Muetzel, L. Caros, and K. O. Lim, “Diffusion tensor imaging in children with fetal alcohol spectrum disorders,” Alcohol Clin. Exp. Res. 30(10), 1799–806 (2006).
9. J. E. Lee, E. D. Bigler, A. L. Alexander, M. Lazar, M. B. DuBray, M. K. Chung, M. Johnson, J. Morgan, J. N. Miller, W. M. McMahon, J. Lu, E. K. Jeong, and J. E. Lainhart, “Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism,” Neurosci. Lett. 424(2), 127–132 (2007).
11. K. Kantarci, R. Avula, M. L. Senjem, A. R. Samikoglu, B. Zhang, S. D. Weigand, S. A. Przybelski, H. A. Edmonson, P. Vemuri, D. S. Knopman, T. J. Ferman, B. F. Boeve, R. C. Petersen, and C. R. Jack Jr., “Dementia with Lewy bodies and Alzheimer disease: Neurodegenerative patterns characterized by DTI,” Neurology 74(22), 1814–1821 (2010).
12. S. Mori and P. C. van Zijl, “Fiber tracking: Principles and strategies–a technical review,” NMR Biomed. 15(7–8), 468–480 (2002).
13. S. Zhang, S. Correia, and D. H. Laidlaw, “Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber-clustering method,” IEEE Trans. Vis. Comput. Graph. 14(5), 1044–1053 (2008).
14. 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 17(1), 77–94 (2002).
16. L. Concha, D. W. Gross, and C. Beaulieu, “Diffusion tensor tractography of the limbic system,” AJNR Am. J. Neuroradiol. 26, 2267–2274 (2005).
17. Z. Ding, J. C. Gore, and A. W. Anderson, “Classification and quantification of neuronal fiber pathways using diffusion tensor MRI,” Magn. Reson. Med. 49(4), 716–721 (2003).
18. A. Brun, H. Knutsson, H. J. Park, M. E. Shenton, and C. F. Westin, “Clustering fiber traces using normalized cuts,” Lect. Notes Comput. Sci. 3216, 368–375 (2004).
19. L. O’Donnell and C. F. Westin, “White matter tract clustering and correspondence in populations,” Medical Image Computing Computer-Assisted Intervention (MICCAI) (Springer, Heidelberg, 2005), pp. 140–147.
20. I. Corouge, P. T. Fletcher, S. Joshi, S. Gouttard, and G. Gerig, “Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis,” Med. Image Anal. 10(5), 786–798 (2006).
21. M. Maddah, W. E. L. Grimson, S. K. Warfield, and W. M. Wells, “A unified framework for clustering and quantitative analysis of white matter fiber tracts,” Med. Image Anal. 12(2), 191–202 (2008).
22. U. Ziyan, M. R. Sabuncu, W. E. Grimson, and C. F. Westin, “Consistency clustering: A robust algorithm for group-wise registration, segmentation and automatic atlas construction in diffusion MRI,” Int. J. Comput. Vis. 85(3), 279–290 (2009).
25. D. Wassermann, L. Bloy, E. Kanterakis, R. Verma, and R. Deriche, “Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers,” Neuroimage 51(1), 228–241 (2010).
26. H. J. Park, M. Kubicki, C. F. Westin, I. F. Talos, A. Brun, S. Peiper, R. Kikinis, F. A. Jolesz, R. W. McCarley, and M. E. Shenton, “Method for combining information from white matter fiber tracking and grey matter parcellation,” AJNR Am. J. Neuroradiol. 25(8), 1318–1324 (2004).
27. Y. Xia, U. Turken, S. L. Whitfield-Gabrieli, and J. D. Gabrieli, “Knowledge-based classification of neuronal fibers in entire brain,” Medical Image Computing Computer-Assisted Intervention (MICCAI) (Springer, Heidelberg, 2005), pp. 205–212.
28. M. Maddah, A. U. J. Mewes, S. Haker, W. E. L. Grimson, and S. K. Warfield, “Automated atlas-based clustering of white matter fiber tracts from DTMRI,” Medical Image Computing Computer-Assisted Intervention (MICCAI) (Springer, Heidelberg, 2005), pp. 188–195.
29. Q. Xu, A. W. Anderson, J. C. Gore, and Z. Ding, “Unified bundling and registration of brain white matter fibers,” IEEE Trans. Med. Imaging 28(9), 1399–1411 (2009).
30. S. Achard, R. Salvador, B. Whitcher, J. Suckling, and E. Bullmore, “A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs,” J. Neurosci. 26(1), 63–72 (2006).
31. M. Guye, F. Bartolomei, and J. P. Ranjeva, “Imaging structural and functional connectivity: Towards a unified definition of human brain organization?” Curr. Opin. Neurol. 21(4), 393–403 (2008).
32. D. K. Jones and C. Pierpaoli, “Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach,” Magn. Reson. Med. 53(5), 1143–1149 (2005).
34. A. W. Anderson, “Theoretical analysis of the effects of noise on diffusion tensor imaging,” Magn. Reson. Med. 46(6), 1174–1188 (2001).
35. C. Poupon, C. A. Clark, V. Frouin, J. Régis, I. Bloch, D. Le Bihan, and J. Mangin, “Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles,” Neuroimage 12(2), 184–195 (2000).
36. Z. Ding, J. C. Gore, and A. W. Anderson, “Reduction of noise in diffusion tensor images using anisotropic smoothing,” Magn. Reson. Med. 53(2), 485–490 (2005).
38. A. L. Alexander, K. M. Hasan, M. Lazar, J. S. Tsuruda, and D. L. Parker, “Analysis of partial volume effects in diffusion-tensor MRI,” Magn. Reson. Med. 45(5), 770–780 (2001).
40. A. W. Anderson, “Measurement of fiber orientation distributions using high angular resolution diffusion imaging,” Magn. Reson. Med. 54(5), 1194–1206 (2005).
42. T. E. Behrens, H. J. Berg, S. Jbabdi, M. F. Rushworth, and M. W. Woolrich, “Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?” Neuroimage 34(1), 144–155 (2007).
43. A. Mishra, A. W. Anderson, X. Wu, J. C. Gore, and Z. Ding, “An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit,” Med. Phys. 37(8), 4274–4287 (2010).
45. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. Ser. B (Stat. Methodol.) 39(1), 1–38 (1977).
46. K. Rohr, H. S. Stiehl, R. Sprengel, T. M. Buzug, J. Weese, and M. H. Kuhn, “Landmark-based elastic registration using approximating thin-plate splines,” IEEE Trans. Med. Imaging 20(6), 526–534 (2001).
47. P. Hagmann, L. Cammoun, X. Gigandet, S. Gerhard, P. Ellen Grant, V. Wedeen, R. Meuli, J. P. Thiran, C. J. Honey, and O. Sporns, “MR connectomics: Principles and challenges,” J. Neurosci. Methods 194(1), 34–45 (2010).
Article metrics loading...
Studying white matter fibers from diffusion tensor imaging (DTI) often requires them to be grouped into bundles that correspond to coherent anatomic structures, particularly bundles that connect cortical/subcortical basic units. However, traditional fiber clustering algorithms usually generate bundles with poor anatomic correspondence as they do not incorporate brain anatomic information into the clustering process. On the other hand, image registration-based bundling methods segment fiber bundles by referring to a coregistered atlas or template with prelabeled anatomic information, but these approaches suffer from the uncertainties introduced from misregistration and fiber tracking errors and thus the resulting bundles usually have poor coherence. In this work, a bundling algorithm is proposed to overcome the above issues.
The proposed algorithm combines clustering- and registration-based approaches so that the bundle coherence and the consistency with brain anatomy are simultaneously achieved. Moreover, based on this framework, a groupwise fiber bundling method is further proposed to leverage a group of DTI data for reducing the effect of the uncertainties in a single DTI data set and improving cross-subject bundle consistency.
Using the Montreal Neurological Institute template, the proposed methods are applied to building a full brain bundle network that connects cortical/subcortical basic units. Based on several proposed metrics, the resulting bundles show promising bundle coherence and anatomic consistency as well as improved cross-subject consistency for the groupwise bundling.
A fiber bundling algorithm has been proposed in this paper to cluster a set of whole brain fibers into coherent bundles that are consistent to the brain anatomy.
Full text loading...
Most read this month