Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels
NeuroImage 20 (2003) 1995–2009 www.elsevier.com/locate/ynimg Spatial normalization of diffusion tensor MRI using multiple channels Hae-Jeong Park,a,b Marek Kubicki,a,b Martha E. Shenton,a,b Alexandre Guimond,c Robert W. McCarley,a Stephan E. Maier,d Ron Kikinis,b Ferenc A. Jolesz,d and Carl-Fredrik Westinb,* a Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA Health Care System-Brockton Division, Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA. b Surgical Planning Laboratory, MRI Division, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. c Center for Neurological imaging, MRI Division, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. d Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA. Received 10 March 2003; revised 30 July 2003; accepted 5 August 2003 Abstract Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components.
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