A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts

Overview

Journal Med Image Anal

Publisher Elsevier

Specialty Radiology

Date 2008 Jan 9

PMID 18180197

Citations 57

Authors

Mahnaz Maddah

W Eric L Grimson

Simon K Warfield

William M Wells

Affiliations

Soon will be listed here.

Abstract

We present a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.

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References

Werring D, Clark C, Barker G, Thompson A, Miller D . Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology. 1999; 52(8):1626-32. DOI: 10.1212/wnl.52.8.1626. View

Huang H, Zhang J, Wakana S, Zhang W, Ren T, Richards L . White and gray matter development in human fetal, newborn and pediatric brains. Neuroimage. 2006; 33(1):27-38. DOI: 10.1016/j.neuroimage.2006.06.009. View

Gerig G, Gouttard S, Corouge I . Analysis of brain white matter via fiber tract modeling. Conf Proc IEEE Eng Med Biol Soc. 2007; 2004:4421-4. DOI: 10.1109/IEMBS.2004.1404229. View

ODonnell L, Westin C . White matter tract clustering and correspondence in populations. Med Image Comput Comput Assist Interv. 2006; 8(Pt 1):140-7. View

Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M, Scotti G . White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry. 2002; 72(6):742-6. PMC: 1737921. DOI: 10.1136/jnnp.72.6.742. View

Park H, Kubicki M, Shenton M, Guimond A, McCarley R, Maier S . Spatial normalization of diffusion tensor MRI using multiple channels. Neuroimage. 2003; 20(4):1995-2009. PMC: 2811885. DOI: 10.1016/j.neuroimage.2003.08.008. View

Alexander D, Pierpaoli C, Basser P, Gee J . Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans Med Imaging. 2001; 20(11):1131-9. DOI: 10.1109/42.963816. View

Salat D, Tuch D, Greve D, van der Kouwe A, Hevelone N, Zaleta A . Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging. 2005; 26(8):1215-27. DOI: 10.1016/j.neurobiolaging.2004.09.017. View

Basser P, Pajevic S, Pierpaoli C, Duda J, Aldroubi A . In vivo fiber tractography using DT-MRI data. Magn Reson Med. 2000; 44(4):625-32. DOI: 10.1002/1522-2594(200010)44:4<625::aid-mrm17>3.0.co;2-o. View

10.

Berman J, Mukherjee P, Partridge S, Miller S, Ferriero D, Barkovich A . Quantitative diffusion tensor MRI fiber tractography of sensorimotor white matter development in premature infants. Neuroimage. 2005; 27(4):862-71. DOI: 10.1016/j.neuroimage.2005.05.018. View

11.

Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G . Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Med Image Anal. 2006; 10(5):786-98. DOI: 10.1016/j.media.2006.07.003. View

12.

Batchelor P, Calamante F, Tournier J, Atkinson D, Hill D, Connelly A . Quantification of the shape of fiber tracts. Magn Reson Med. 2006; 55(4):894-903. DOI: 10.1002/mrm.20858. View

13.

Smith S, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols T, Mackay C . Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006; 31(4):1487-505. DOI: 10.1016/j.neuroimage.2006.02.024. View

14.

Jellison B, Field A, Medow J, Lazar M, Salamat M, Alexander A . Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol. 2004; 25(3):356-69. PMC: 8158568. View

15.

Brun A, Knutsson H, Park H, Shenton M, Westin C . Clustering Fiber Traces Using Normalized Cuts. Med Image Comput Comput Assist Interv. 2010; 3216/2004(3216):368-375. PMC: 3296487. DOI: 10.1007/b100265. View

16.

Goldberg-Zimring D, Mewes A, Maddah M, Warfield S . Diffusion tensor magnetic resonance imaging in multiple sclerosis. J Neuroimaging. 2005; 15(4 Suppl):68S-81S. DOI: 10.1177/1051228405283363. View

17.

Ding Z, Gore J, Anderson A . Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Magn Reson Med. 2003; 49(4):716-21. DOI: 10.1002/mrm.10415. View

18.

Maddah M, Mewes A, Haker S, Grimson W, Warfield S . Automated atlas-based clustering of white matter fiber tracts from DTMRI. Med Image Comput Comput Assist Interv. 2006; 8(Pt 1):188-95. DOI: 10.1007/11566465_24. View

19.

Schocke M, Seppi K, Esterhammer R, Kremser C, Mair K, Czermak B . Trace of diffusion tensor differentiates the Parkinson variant of multiple system atrophy and Parkinson's disease. Neuroimage. 2004; 21(4):1443-51. DOI: 10.1016/j.neuroimage.2003.12.005. View

20.

Park H, Westin C, Kubicki M, Maier S, Niznikiewicz M, Baer A . White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study. Neuroimage. 2004; 23(1):213-23. PMC: 2794419. DOI: 10.1016/j.neuroimage.2004.04.036. View