From diffusion tractography to quantitative white matter tract measures: a reproducibility study O. Ciccarelli, a G.J.M. Parker, b A.T. Toosy, a C.A.M. Wheeler-Kingshott, a G.J. Barker, a P.A. Boulby, c D.H. Miller, a and A.J. Thompson a, * a NMR Research Unit, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK b Imaging Science and Biomedical Engineering, University of Manchester, Oxford Road, Manchester M13 9PT, UK c MRI Unit, National Society for Epilepsy, Chalfont St. Peter, Gerrards Cross, Buckinghanshire, SL9 0RJ, UK Received 13 March 2002; revised 12 September 2002; accepted 21 October 2002 Abstract The aim of this study is to propose methods for assessing the reproducibility of diffusion tractography algorithms in future clinical studies and to show their application to the tractography algorithm developed in our unit, fast marching tractography (FMT). FMT estimates anatomical connectivity between brain regions using the information provided by diffusion tensor imaging. Three major white-matter pathways were investigated in 11 normal subjects—anterior callosal fibers, optic radiations, and pyramidal tracts. FMT was used to generate maps of connectivity metric, and regions of voxels with highest connectivity metric to an anatomically defined starting point were identified for each tract under investigation. The reproducibilities of tract-“normalized” volume (NV) and fractional anisotropy (FA) measurements were assessed over such regions. The values of tract volumes are consistent with the postmortem data. Coefficients of variation (CVs) for FA and NV ranged from 1.7 to 7.1% and from 2.2 to 18.6%, respectively. CVs were lowest in the anterior callosal fibers (range: 1.7– 7.8%), followed by the optic radiations (range: 1.2–18.6%) and pyramidal tracts (range: 2.6 –15.5%), suggesting that fiber organization plays a role in determining the level of FMT reproducibility. In conclusion, these findings underline the importance of assessing the reliability of diffusion tractography before investigating white matter pathology. © 2003 Elsevier Science (USA). All rights reserved. Introduction Diffusion tensor imaging (DTI) is an MRI technique that provides information about the random thermal motion of water molecules in vivo (Basser et al., 1994). In human tissues, water diffusion is not free in all directions but hindered and restricted by the presence of barriers, includ- ing cell cytostructure and membranes. For example, in the white matter regions of the brain, where the neuronal pro- jections or axons are similarly aligned, water diffusion is generally greater in the direction parallel to axons than perpendicular to them. This property is termed diffusion anisotropy (Moseley et al., 1990; Chenevert et al., 1990). In contrast, in the gray matter regions, which are characterized by a less ordered tissue structure, diffusion tends to be less anisotropic and more uniform in all directions. Therefore, DTI allows in vivo identification of white matter and gray matter regions and it has been used in these areas to study pathological changes, including those occurring in multiple sclerosis (Horsfield et al., 1998; Tievsky et al., 1999; Wer- ring et al., 1999; Ciccarelli et al., 2001), tumors (Wiesh- mann et al., 1999; Bastin et al., 1999; Inglis et al., 1999), amyotrophic lateral sclerosis (Ellis et al., 1999), cerebral ischemia (Chabriat et al., 1999; Sorensen et al., 1999; Jones et al., 1999b; Helenius et al., 2002), and developmental malformations of the cortex (Eriksson et al., 2001). Since DTI is able to detect at the macroscopic scale of a voxel the extent of directional bias of diffusion occurring at the microscopic level, it can distinguish between regions where fibers are highly aligned in the voxel from those where fibers are less coherent. However, although DTI provides directional information at the voxel level, it pro- vides no explicit information about the connection between * Corresponding author. Fax: +44-207-8136505. E-mail address: a.thompson@ion.ucl.ac.uk (A.J. Thompson). R Available online at www.sciencedirect.com NeuroImage 18 (2003) 348 –359 www.elsevier.com/locate/ynimg 1053-8119/03/$ – see front matter © 2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S1053-8119(02)00042-3