Subject: WashCAS news: next talk will be Tuesday May 20th on Diffusion Tensor Imaging Dear WashCAS attendee: After a brief break due to events in the Middle East, we are back on schedule with the WashCAS talks. We are pleased to announce that the May 20th WashCAS talk will be given by Peter Basser, PhD, of the National Institute of Child Health and Human Development. The topic will be "Diffusion Tensor Imaging". The abstract is pasted below and is on the web site at www.washcas.org. We may also have a special guest presentation at the beginning of the meeting - details will be forthcoming. The meeting will be held in the Lister Hill Center, Building 38A, behind the National Library of Medicine on the NIH Campus in Bethesda. The visitor's entrance is on South Drive off of Wisconsin Avenue, next to the metro stop. Drivers and passengers should be prepared to present photo IDs. Vehicle hoods and trunks may be opened for inspection. For directions, see http://www.nlm.nih.gov/about/directions.html The meeting schedule is: 6:30 - 7:00 pm: refreshments and food in lobby 7:00 - 8:00 pm: speaker and discussion Please remember that absolutely no food is allowed in the meeting room. At this point, we don't anticipate any problems with holding the meeting at the Lister Hill Center. If the situtation changes, we will move the meeting to a local Bethesda hotel and notify you. If you are even considering attending, please RSVP by replying to this email or let one of the society officers know. This is not required, but helps us in planning the food order as well as giving a list of potential attendees to the security personnel. We are now using our mailing list for these announcements. If you wish to unsubscribe, please send email to cleary@georgetown.edu. For those who did not receive this announcement and wish to subscribe to the mailing list, ask them to send email to join@washcas.org and they will be automatically added. WashCAS meetings are held every other month on the third Tuesday of the month. For WashCAS news, see www.washcas.org Sincerely, Kevin Cleary, PhD Georgetown University ISIS Center (202) 687-8253 cleary@georgetown.edu James Burgess, M.D. Neurosurgery Inova Fairfax Hospital (703) 718-0314 burgessj@pol.net Gerald Higgins, Ph.D. Simquest (301) 565-2033 Higgins@simquest.com Terry Yoo, PhD National Library of Medicine (301) 435-3268 yoo@nlm.nih.gov Diffusion-Tensor MRI: An Overview Peter Basser, Ph.D. Section on Tissue Biophysics & Biomimetics, NICHD National Institutes of Health, Bethesda, MD, USA pjbasser@helix.nih.gov This talk deals with the measurement (or estimation) of the effective diffusion tensor (1) of water in tissues. We review the history of diffusion measurements using NMR and MRI methods, the definition and physical interpretation of the effective diffusion tensor, as well as quantitative MR parameters derived from it, such as the Trace and measures of diffusion anisotropy (2). We will also provide an overview of several biological and clinical applications of DT-MRI. The NMR measurement of the effective diffusion tensor of water, D, within each voxel of an imaging volume, and its analysis and display is called Diffusion Tensor MRI (DT-MRI) or Diffusion Tensor Imaging (DTI) (3). This measurement can be obtained in vivo, non-invasively, without requiring exogenous contrast agents. The MR measurement of D in tissues can provide unique biologically and clinically relevant information that is not available from other imaging modalities, particularly quantitative parameters that help characterize tissue composition, the physical properties of tissue constituents, and tissue microstructure and its architectural organization. In tissues, such as brain gray matter, where the measured apparent diffusivity is approximately independent of the orientation of the tissue (i.e. isotropic) at a voxel length scale, it is usually sufficient to describe the diffusion characteristics using a single(scalar) apparent diffusion coefficient (ADC). However, in anisotropic media, such as skeletal and cardiac muscle (4-6), and in white matter (7-9) where the measured apparent diffusivity depends upon the orientation of the tissue, a single ADC is not adequate to characterize the orientation-dependent water mobility. For such a free anisotropic diffusion process, one can replace the scalar diffusion coefficient appearing in Fick's law with a symmetric effective or apparent diffusion tensor of water, D (e.g., see (10)). In DT-MRI, D, is estimated from a series of DW images and their corresponding b-matrices (that summarize the effects of all applied gradients on the amount of diffusion weighting on each element of D) (3). DT-MRI is inherently three-dimensional. To measure D, one must apply diffusion gradients along at least six non-collinear, non-coplanar directions (1). Quantitative parameters can be extracted from diffusion-tensor MRI data. Intrinsic quantities can be found to characterize distinct features describing the size, shape, orientation, or pattern of rms diffusion ellipsoids within an imaging volume. These quantities are rotationally invariant, i.e., independent of the orientation of the tissue structures, the patient's body within the MR magnet, the applied diffusion sensitizing gradients, and the choice of the laboratory coordinate system in which the components of the diffusion tensor and magnet field gradients are measured (3; 11). Some examples are Trace(D), which is proportional to the orientationally-averaged intrinsic diffusivity or mean ADC; the eigenvalues of D, which are the principal diffusivities along the local principal axes; the eigenvectors of D, which define the orientations of the local principal axes; as well as many parameters and constructs derived from these quantities. For instance, color maps that indicate the local fiber-tract orientation, are created by combining information contained in the eigenvector associated with the large principal diffusivity and a measure of diffusion anisotropy (12-15). Fiber tract trajectories can be constructed from D data by generating streamlines that follow the direction of maximum diffusivity (e.g., see (3; 16-28). Probabilistic schemes for tractograph and connectivity analysis (29-33) use D data to construct fiber orientation distributions. The main artifacts in obtaining DT-MRI data are those associated with acquiring the DWIs from which D is measured. These include subject motion, eddy currents, magnetic susceptibility effects, and image noise. Hardware issues, such as background gradients, and gradient miscalibration should also be considered. Some errors and artifacts that are peculiar to tractography will be described. The majority of applications of DT-MRI to date have been to diagnosing and assessing various neurological disorders (e.g., chronic and acute ischemia (stroke), Wallerian degeneration, ALS, Alzheimers disease) as well as behavior and cognitive disorders (e.g., schizophrenia, dyslexia, ADD, ADHD). More recently a number of therapeutic applications have emerged. The most notable and promising is in the assessment of tumor type and in the planning of surgical procedures to remove cancerous tissue. DT-MRI provides new information with which to probe tissue structure at different levels of hierarchical organization. New structural parameters provided by DT-MRI, such as maps of the principal diffusivities of D, its Trace, measures of the degree of diffusion anisotropy and organization, and estimates of fiber direction are all helping to advance our understanding of nerve pathways in the CNS, as well as the anatomical organization of some soft fibrous tissues. Biography Peter J. Basser is Senior Investigator and Chief of the Section on Tissue Biophysics and Biomimetics within the National Institute of Child Health and Human Development (NICHD). Peter received his A.B, S.M., and Ph.D. from Harvard University in '80,'82 and '86 respectively. In 1986 he received a Staff Fellow appointment in the Biomedical Engineering and Instrumentation Program (BEIB) at the National Institutes of Health (NIH). In 1998, he joined NICHD in his current position. Dr. Basser's research focuses primarily on transport processes in tissues. He has contributed to the understanding of the mechanisms of action of electromagnetic fields on excitable tissues (magnetic stimulation), measuring and characterizing diffusive transport of water in tissues using magnetic resonance methods, and explaining the functional properties of extracellular matrix in terms of the osmotic properties of its constituents. Bibliography 1. P.J. Basser, J. Mattiello, and D. Le Bihan. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B 103(3), 247-54 (1994). 2. P.J. Basser. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8(7-8), 333-44 (1995). 3. P.J. Basser, J. Mattiello, and D. Le Bihan. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259-67 (1994). 4. G.G. Cleveland, D.C. Chang, C.F. Hazlewood, and H.E. Rorschach. Nuclear magnetic resonance measurement of skeletal muscle: anisotropy of the diffusion coefficient of the intracellular water. Biophys. J. 16(9), 1043-53 (1976). 5. J.E. Tanner. Self diffusion of water in frog muscle. Biophys J 28(1), 107-16 (1979). 6. L. Garrido, V.J. Wedeen, K.K. Kwong, U.M. Spencer, and H.L. Kantor. Anisotropy of water diffusion in the myocardium of the rat. Circ Res 74(5), 789-93 (1994). 7. M.E. Moseley, Y. Cohen, J. Kucharczyk, J. Mintorovitch, H.S. Asgari, M.F. Wendland, J. Tsuruda, and D. Norman. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 176(2), 439-45 (1990). 8. M.E. Moseley, J. Kucharczyk, H.S. Asgari, and D. Norman. Anisotropy in diffusion-weighted MRI. Magn Reson Med 19(2), 321-6 (1991). 9. R.M. Henkelman, G.J. Stanisz, J.K. Kim, and M.J. Bronskill. Anisotropy of NMR properties of tissues. Magn Reson Med 32(5), 592-601 (1994). 10. J. Crank, "The mathematics of diffusion". Oxford University Press, Oxford, England (1975). 11. P.J. Basser, and D. Le Bihan. Fiber orientation mapping in an anisotropic medium with NMR diffusion spectroscopy. 11th Annual Meeting of the SMRM; 1992; Berlin. p 1221. 12. D.K. Jones, S.C.R. Williams, and M.A. Horsfield. Full Representation of White-Matter Fibre Direction on One Map Via Diffusion Tensor Analysis. 5th ISMRM Meeting; 1997; Vancouver. p 1743. 13. C. Pierpaoli. Oh No! One More Method for Color Mapping of Fiber Tract Direction Using Diffusion MR Imaging Data. 5th ISMRM; 1997; Vancouver. p 1741. 14. N. Makris, A.J. Worth, A.G. Sorensen, G.M. Papadimitriou, O. Wu, T.G. Reese, V.J. Wedeen, T.L. Davis, J.W. Stakes, V.S. Caviness and others. Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging. 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Zijl. 3D Reconstruction of Axonal Fibers from Diffusion Tensor Imaging using Fiber Assignment by Continuous Tracking (FACT). 8th Annual Meeting of the ISMRM; 1999; Philadelphia, PA. p 320. 25. T.E. Conturo, N.F. Lori, T.S. Cull, E. Akbudak, A.Z. Snyder, J.S. Shimony, R.C. McKinstry, H. Burton, and M.E. Raichle. Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A 96(18), 10422-7 (1999). 26. P.J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi. In Vivo Fiber-Tractography in Human Brain Using Diffusion Tensor MRI (DT-MRI) Data. Magn. Reson. Med. 44(4), 625-632 (2000) 27. S. Mori, W.E. Kaufmann, G.D. Pearlson, B.J. Crain, B. Stieltjes, M. Solaiyappan, and P.C. van Zijl. In vivo visualization of human neural pathways by magnetic resonance imaging. Ann Neurol 47(3), 412-4. (2000). 28. C. Poupon, J. Mangin, C.A. Clark, V. Frouin, J. Regis, D. Le Bihan, and I. Bloch. Towards inference of human brain connectivity from MR diffusion tensor data. 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