Abstract
Abstract Diffusion Tensor MRI (DTI) is a special MR imaging technique where the second order symmetric diffusion tensors that are correlated with the underlying fi-brous structure (eg. the nerves in brain), are computed based on DiffusionWeighted MR Images (DWI). DTI is the only in vivo imaging technique that provides information about the network of nerves in brain. The computed tensors describe the local diffusion pattern of water molecules via a 3D Gaussian distribution in space. The most common analysis and visualization technique is tractography, which is a numerical integration of the principal diffusion direction (PDD) that attempts to reconstruct fibers as streamlines. Despite its simplicity and ease of interpretation, tractography algorithms suffer from several drawbacks mainly due to ignoring the information in the underlying spatial distribution but using the PDD only. An alternative to tractography is connectivity which aims at computing probabilistic connectivity maps based on the above mentioned 3D Gaussian distribution as described by the DTI data. However, the computational cost is high and the resulting maps are usually hard to visualize and interpret. This chapter discusses these two approaches and introduces two new tractography techniques, namely the Lattice-of-Springs (LoS) method that exploits the connectivity approach and the Split & Merge Tractography (SMT) that attempts to combine the advantages of tractography and connectivity.
An erratum to this chapter is available at http://dx.doi.org/10.1007/978-1-84882-299-3_22
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Stejskal E.O., Tanner J.E. (1965) Spin diffusion measurements:spin echoes in the presence of a time dependent field gradient. J Chem Phys 42:288-292.
Le Bihan D. (1995) Diffusion, perfusion and functional magnetic resonance imaging. J Mal Vasc 20:203-214.
Moseley M.E., Cohen Y., Mintorovitch J. et al. (1990) Early detection of regional cerebral ischemia in cats: comparison of diffusion- and T2-weighted MRI and spectroscopy. Magn Reson Med 14:330-346.
Lansberg M.G., Norbash A.M., Marks M.P. et al. (2000) Advantages of adding diffusion-weighted magnetic resonance imaging to conventional magnetic resonance imaging for evaluating acute stroke. Arch Neurol 57:1311-1316.
Guo A.C., Provenzale J.M., Cruz L.C. et al. (2001) Cerebral abscesses: investigation using apparent diffusion coefficient maps. Neuroradiology 43:370-374.
Young G.S., Geschwind M.D., Fischbein N.J. et al. (2005) Diffusion-weighted and fluid-attenuated inversion recovery imaging in Creutzfeldt-Jakob disease: high sensitivity and specificity for diagnosis. Am J Neuroradiol 26:1551-1562.
Moseley M.E., Kucharczyk J., Asgari H.S. et al. (1991) Anisotropy in diffusion-weighted MRI. Magn Reson Med 19:321-326.
Bammer R., Fazekas F. (2002) Diffusion imaging in multiple sclerosi. Neuroimaging Clin N Am 12:71-106.
Ellis C.M., Simmons A., Jones D.K. et al. (1999) Diffusion tensor MRI assesses corticospinal tract damage in ALS. Neurology 53:1051-1058
Barnea-Goraly N., Eliez S., Hedeus M. et al. (2003) White matter tract alterations in fragile X syndrome: preliminary evidence from diffusion tensor imaging. American Journal of Medical Genetics. Part B, Neuropsychiatric genetics 118:81-88.
Barnea-Goraly N., Kwon H., Menon V. et al. (2004) White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biological psychiatry 55:323-326.
Barnea-Goraly N., Eliez S., Menon V. et al. (2005) Arithmetic ability and parietal alterations: a diffusion tensor imaging study in velocardiofacial syndrome. Brain Res Cogn Brain Res. 25:735-740.
Pribam K., MacLean P. (1953) Neuronographic analysis of medial and basal cerebral corte. J Neurophysiol 16:324-340.
Whitlock D.G., Nauta W.J.H. (1956) Subcortical projections from temporal neocortex in Macaca mulatto. J Comp Neurol 106:183-212.
Turner B.H., Mishkin M., Knapp M. (1980) Organization of the amygdalopetal projections from modality-specific cortical association areas in the monkey. J Comp Neurol 191:515-543.
Yagishita A., Nakano I., Oda M. et al. (1994) Location of the corticospinal tract in the internal capsule at MR imaging. Radiology 191:455-460.
Basser P.J. (1995) Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8:333-344.
Basser P.J., Mattiello J., LeBihan D. (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103:247-254.
Basser P.J., Pierpaoli C. (1996) Microstructural and physiological features of tissues elucidated by quantitative diffusion tensor MRI. J Magn Reson B 111:209-219.
Basser P.J., Pajevic S., Pierpaoli C. et al. (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44:625-632.
Conturo T.E., Lori N.F., Cull T.S. et al. (1999) Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci USA 96:10422-10427.
Jones D.K., Simmons A., Williams S.C. et al. (1999) Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn Reson Med 42:37-41
Liu C., Bammmer R., Acar B. et al (2004) Characterizing non-Gaussian diffusion by using generalized diffusion tensors. Magnetic Resonance in Medicine 51:924-937
Tench C.R., Morgan P.S., Wilson M. et al. (2002) White matter mapping using diffusion tensor MRI. Magnetic Resonance in Medicine 47:967-972
Behrens T.E., Johansen-Berg H., Woolrich M.W., et al (2003) Non-invasive mapping of connections between human Thalamus and Cortex using diffusion imaging. Nature Neuroscience 6:750-757
Bjornemo M., Brun A., Kikinis R. et al (2002) Regularized stochastic white matter tractography using diffusion tensor MRI. In: Lecture Notes in Computer Science, 2488:435-442, Springer-Verlag (Proceedings of MICCAI 2002, Tokyo, Japan)
Friman O., Westin C.F. (2005) Uncertainty in white matter fiber tractography In: Lecture Notes in Computer Science, 3749:107-114, Springer-Verlag (Proceedings of MICCAI 2005, Palm Springs, CA, USA)
Hagmann P., Thiran J.P., Jonasson L. et al (2003) DTI mapping of human brain connectivity: statistical fibre tracking and virtual dissection. Neuroimage 19:545-554
Lazar M, Alexander A.L. (2002) White matter tractography using random vector (RAVE) perturbation. In: Proceedings of ISMRM Annual Meeting, Honolulu, HI, USA.
Parker G.J., Haroon H.A., Wheeler-Kingshott C.A. (2003) A Framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. Journal of Magnetic Resonance Imaging 18:242-254.
Behrens T.E., Berg H.J., Jbabdi S. et al (2007) Probabilistic diffusion tractography with multiple fibre orientations: What Can We Gain? Neuroimage 34:144-155
Jbabdi S., Woolrich M.W., Andersson J.L. et al (2007) A bayesian framework for global tractography. Neuroimage 37:116-129
Sherbondy, A., Dougherty R. et al. (2008). ConTrack: Finding the most likely pathways between brain regions using diffusion tractography. Journal Of Vision 8:1-16
Koch M.A., Norris D.G., Hund-Georgiadis M. (2002) An investigation of functional and anatomical connectivity using magnetic resonance imaging. Neuroimage 16:241-250
Hagmann P., Thiran J.P., Vandergheynst P. et al (2000) Statistical fiber Tracking on DT-MRI Data as a Potential Tool for Morphological Brain Studies. ISMRM Workshop on Diffusion MRI: Biophysical Issues
Chung M.K., Lazar M., Alexander A.L. et al (2003) Probabilistic connectivity measure in diffusion tensor imaging via anisotropic kernel smoothing. Technical Report No:1081, University of Wisconsin
Lenglet C., Deriche R. , Faugeras O. (2003) Diffusion tensor magnetic resonance imaging: brain connectivity mapping. Technical Report No: 4983, INRIA, Sophia-Antipolis, France
Lenglet, C., M. Rousson, et al. (2006) DTI segmentation by statistical surface evolution. IEEE Transactions on Medical Imaging 25(6):685
Bozkaya U., Acar B. (2007) SMT: split & merge fiber tractography for DT-MRI. In: Lecture Notes in Computer Science, 4792:153-160, Springer (Proceedings of MICCAI 2007, Brisbane, Australia)
Bozkaya U., Acar B. (2006) SMT: mplit/merge fiber tractography for MR-DTI. In: Proceedings of ESMRMB 2006, Warsaw, Poland.
Yörük E., Acar B., Bammer R. (2005) A physical model for MR-DTI based connectivity map computation. In: Lecture Notes in Computer Science, 3749/1:213-220, Springer (Proceedings of MICCAI 2005, Palm Springs, CA, USA)
Bozkaya U (2006) SMT: split/merge fiber tractography for MR-DTI. M.S. Thesis, Boğiçi University, Biomedical Engineering Institute, Istanbul, Turkey
Lazar M, Alexander A.L. (2005) Bootstrap white matter tractography (BOOT-TRAC). Neuroimage 24:524-532.
Chib S., Greenberg E. (1995) Understanding the Metropolis-Hastings algorithm. The American Statistician 49:327-335
Sherbondy A., Akers D., Mackenzie R. et al (2005) Exploring connectivity of the brain’s white matter with dynamic queries. IEEE Trans. Vis. Comput. Graph. 11:419-430.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this chapter
Cite this chapter
Acar, B., Yörük, E. (2009). DT-MRI Connectivity and/or Tractography?: Two New Algorithms. In: Aja-Fernández, S., de Luis García, R., Tao, D., Li, X. (eds) Tensors in Image Processing and Computer Vision. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-299-3_16
Download citation
DOI: https://doi.org/10.1007/978-1-84882-299-3_16
Publisher Name: Springer, London
Print ISBN: 978-1-84882-298-6
Online ISBN: 978-1-84882-299-3
eBook Packages: Computer ScienceComputer Science (R0)