Abstract
Existing methods for fiber tracking, interactive bundling and editing from Diffusion Magnetic Resonance Images (DMRI) reconstruct white matter fascicles using groups of virtual pathways. Classical numerical fibers suffer from image noise and cumulative tracking errors. 3D visualization of bundles of fibers reveals structural connectivity of the brain; however, extensive human intervention, tracking variations and errors in fiber sampling make quantitative fascicle comparison difficult. To simplify the process and offer standardized white matter samples for analysis, we propose a new integrated fascicle parcellation and normalization method that combines a generic parametrized volumetric tract model with orientation information from diffusion images. The new technique offers a tract-derived spatial parameter for each voxel within the model. Cross-subject statistics of tract data can be compared easily based on these parameters. Our implementation demonstrated interactive speed and is available to the public in a packaged application.
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Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson B 103, 247–254 (1994)
Lori, N., Akbudak, E., Shimony, J., Cull, T., Snyder, A., Guillory, R., Conturo, T.: Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results. NMR In Biomedicine 15, 493–515 (2002)
Alexander, A.L., Hasan, K.M., Lazar, M., Tsuruda, J.S., Parker, D.L.: Analysis of partial volume effects in diffusion-tensor MRI. Magn. Reson Med. 45, 770–780 (2001)
Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999)
Lazar, M., Weinstein, D.M., Tsuruda, J.S., Hasan, K.M., Arfanakis, K., Meyerand, M.E., Badie, B., Rowley, H.A., Haughton, V., Field, A., Alexander, A.L.: White matter tractography using diffusion tensor deflection. Hum. Brain Mapp. 18, 306–321 (2003)
Catani, M., Howard, R.J., Pajevic, S., Jones, D.K.: Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17, 77–94 (2002)
Faires, J.D., Burden, R.L.: Numerical methods, 3rd edn. Thomson/Brooks/Cole, Pacific Grove, CA (2003)
Jiang, H., van Zijl, P.C.M., Kim, J., Pearlson, G.D., Mori, S.: DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput. Methods Programs Biomed. 81, 106–116 (2006)
Wang, R., Benner, T., Sorensen, A.G., Wedeen, V.J.: Diffusion toolkit: A software package for diffusion imaging data processing and tractography. Int’l Society of Magnetic Resonance in Medicine 15, 3720 (2007)
O’Donnell, L.J., Westin, C.-F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging 26, 1562–1575 (2007)
Yushkevich, P.A., Zhang, H., Simon, T.J., Gee, J.C.: Structure-specific statistical mapping of white matter tracts. Neuroimage 41, 448–461 (2008)
O’Donnell, L.J., Westin, C.-F., Golby, A.J.: Tract-based morphometry for white matter group analysis. Neuroimage 45, 832–844 (2009)
Mohan, V., Sundaramoorthi, G., Tannenbaum, A.: Tubular surface segmentation for extracting anatomical structures from medical imagery. IEEE Trans. Med. Imaging 29, 1945–1958 (2010)
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.J.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505 (2006)
Corouge, I., Fletcher, P.T., Joshi, S., Gouttard, S., Gerig, G.: Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Med. Image Anal. 10, 786–798 (2006)
Ho, H.P., Wang, F., Blumberg, H.P., Staib, L.H.: Fast parametrized volumetric DTI tract parcellation. In: IEEE Int’l Symposium on Biomedical Imaging (2011)
Oppenheim, A.V., Willsky, A.S., Nawab, S.H.: Signals and systems, 2nd edn. Prentice-Hall, Upper Saddle River (1997)
Deoni, A.: Phantom Images for Simulating Tractography Errors, http://cubric.psych.cf.ac.uk/commondti
Jackowski, M., Kao, C., Qiu, M., Constable, R., Staib, L.: White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging. Medical Image Analysis 9, 427–440 (2005)
Papademetris, X., Jackowski, M., Rajeevan, N., Okuda, H., Constable, R., Staib, L.: Bioimage Suite: An integrated medical image analysis suite, Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine
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Ho, H.P., Wang, F., Papademetris, X., Blumberg, H.P., Staib, L.H. (2011). Integrated Parcellation and Normalization Using DTI Fasciculography. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_5
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DOI: https://doi.org/10.1007/978-3-642-23629-7_5
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