Development of a human brain diffusion tensor template

Abstract

The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the development of a white matter atlas. Previous efforts to produce a DTI brain template have been compromised by factors related to image quality, the effectiveness of the image registration approach, the appropriateness of subject inclusion criteria, the completeness and accuracy of the information summarized in the final template. The purpose of this work was to develop a DTI human brain template using techniques that address the shortcomings of previous efforts. Therefore, data containing minimal artifacts were first obtained on 67 healthy human subjects selected from an age-group with relatively similar diffusion characteristics (20–40 years of age), using an appropriate DTI acquisition protocol. Non-linear image registration based on mean diffusion-weighted and fractional anisotropy images was employed. DTI brain templates containing median and mean tensors were produced in ICBM-152 space and made publicly available. The resulting set of DTI templates is characterized by higher image sharpness, provides the ability to distinguish smaller white matter fiber structures, contains fewer image artifacts, than previously developed templates, and to our knowledge, is one of only two templates produced based on a relatively large number of subjects. Furthermore, median tensors were shown to better preserve the diffusion characteristics at the group level than mean tensors. Finally, white matter fiber tractography was applied on the template and several fiber-bundles were traced.

Symmetric diffeomorphic registration of fibre orientation distributions

“…This type of procedure is the first step required in building a white matter atlas. Current state of the art DWI atlases are DTI based and are consequently limited to accurately representing only major white matter bundles in voxels with single fibre populations (Mori et al, 2008;Hecke et al, 2008;Peng et al, 2009;Zhang et al, 2010b). For applications such as automated template-based white matter parcellation, an atlas derived using FODs would provide more accurate information about the location, orientation and partial volume of white matter bundles, and is therefore likely to improve segmentation accuracy.…”

“…Unlike existing DTI derived atlases (Mori et al, 2008;Hecke et al, 2008;Peng et al, 2009;Zhang et al, 2010b), a FOD-derived atlas could be used to map the location, orientation and partial volume of known fibres bundles. Such atlases would offer more information that may increase the accuracy of automated template-based white matter segmentation.…”

DTI Values in Key White Matter Tracts from Infancy through Adolescence

“…[14][15][16] Previous DTI studies of WM development have been limited by small numbers of pediatric patients, inclusion of data from multiple institutions, combination of adult and pediatric data, or inclusion of potentially biasing pathologies without further follow-up. 4,8,13,[17][18][19][20][21][22] The aim of our study was, thus, to report a representative dataset of normal age-related changes in FA and MD values detected in the developing brain, derived from 202 healthy participants ranging in age from birth to adolescence. We propose that these normative data represent true age-appropriate DTI values during the development of susceptible and critical WM tracts.…”

Three-Dimensional Interactive and Stereotactic Human Brain Atlas of White Matter Tracts

“…Several DTI-based atlases and templates have been created including (Catani and Thiebaut de Schotten 2008), (Chiang et al 2008), (Dougherty et al 2005), (Hagmann et al 2003), (Hua et al 2008), (Mori et al 2005), (Mori et al 2008), , (Oishi et al 2011), (Peng et al 2009), (Van Hecke et al 2008, (Verhoeven et al 2010), (Wakana et al 2004), (Zhang et al 2011). They differ in terms of data acquisition (e.g., 6 encoding directions (Hagmann et al 2003) versus 200 directions (Catani and Thiebaut de Schotten 2008)), content and the number of tracts (e.g., 15 tracts in (Verhoeven et al 2010)), methods applied (as addressed above; see also the discussion), parameters (e.g., a high value of FA threshold along with the trajectory angle (Mori et al 2005) versus a low value of FA threshold without the angle (Lawes et al 2008); see also the discussion), visualization (e.g., two-dimensional (2D) versus 3D), tools employed (as listed above), tract clustering (manual (e.g., (Mori et al 2005) versus automatic (e.g., Visser et al 2011), ); see the discussion), brain coverage (the whole brain (e.g., (Mori et al 2005) versus its specific part (e.g., occipital-callosal part (Dougherty et al 2005)), number of subjects (e.g., 1 in (Catani et al 2002), (Hagmann et al 2003), (Mori et al 2005), (Wakana et al 2004), 15 in (Lawes et al 2008), 28 in (Hua et al 2008), 40 in (Thiebaut de Schotten et al 2011, 67 in (Zhang et al 2011), and 81 in (Mori et al 2008), ), and applied registration approaches (e.g., the widely used affine (e.g., …”