The white mater consists of axons that connect different areas of the brain. Axons that share similar destinations tend to form large bundles called white matter tracts. The anatomy of prominent tracts, which have a size as large as a few centimeters in the human brain, has been well-characterized in previous anatomical studies using postmortem samples (Dejerine, 1895
; Krieg, 1963
). Recently, it has been shown that many of these tracts can be reconstructed non-invasively and three-dimensionally based on pixel-by-pixel diffusion orientation information obtained from diffusion tensor imaging (DTI) (Basser et al., 2000
; Conturo et al., 1999
; Jones et al., 1999b
; Lazar et al., 2003
; Mori et al., 1999
; Mori et al., 2005
; Parker et al., 2002
; Poupon et al., 2000
; Wakana et al., 2004
). Although it is known that the DTI-based anatomical information is oversimplified compared to the underlying neuroanatomy, this 3D reconstruction technique, often called tractography, is an important tool to delineate the macroscopic architecture of the human brain white matter and investigate its status under pathological conditions.
Tractography, however, has several known limitations. As mentioned above, the raw pixel-by-pixel DTI data is only an approximation of the axonal fiber orientations, and, therefore, the detailed connectivity information obtained from the reconstructed tracts could be inaccurate and the validation is not straightforward (this problem will be referred to as the “accuracy issue” hereafter in this paper). This method may also suffer from reproducibility problems (“precision issue”). Specifically, if the same person is scanned several times or the same data are analyzed multiple times, the results of tractography may differ each time. One of the major sources of this precision issue is the technique’s dependency on manually defined regions of interest (ROIs), or seed pixels. If tractography is performed from every pixel inside the brain, we would obtain millions of streamlines. It is, therefore, a common practice to manually define ROIs to extract only those streamlines that belong to a selected white matter tract. Previous studies have shown that the use of multiple ROIs, based on existing anatomical knowledge, could impose strong anatomical constraints and greatly enhance the precision of tractography results (Huang et al., 2004
; Posner and Dehaene, 1994
). However, the placement of the ROIs requires a fair amount of anatomical knowledge and extensive training. In addition, it is not always straightforward to develop ROI-placement protocols for reproducible reconstruction of tracts of interest that have convoluted trajectories.
In our previous publication, we designed and tested protocols for ROI placements for various white matter tracts and generated protocols for 11 major tracts, which passed the precision tests (intra and inter-rater reproducibility) (Wakana et al., 2007
). One of the limitations of the protocols for manual ROI placement is that the ROIs are usually confined in a 2D plane in one of the three orthogonal viewing angles. Some important tracts, such as the corpus callosum and thalamic radiation, are often difficult to define by a single 2D plane. Smaller association (cortico-cortical) tracts are also difficult to define by this type of approach due to the complex shape of the cortex. Another important limitation of the manual ROI approach is that it can become prohibitively labor-intensive if a comprehensive reconstruction of a large number of tracts in multiple-subject brain data is necessary.
To overcome this limitation, we designed and tested automated placements of 3D ROIs (Zhang et al., 2008
). The concept is simple. In a representative 3D brain image (atlas), 3D ROIs are predefined by an expert (called the Template ROI Set, or TRS). Then, the atlas, which carries the predefined TRS for tractography, is linearly or non-linearly warped to each subject’s data. In our previous publication, we tested this approach for the aforementioned 11 major tracts, for which tractography protocols were well-established. The tractography results, based on the automated ROI placement, were compared to the manual method and excellent agreement between the two methods was found for the 11 major tracts (Zhang et al., 2008
This study is an extension of the previous studies, with several important improvements. First, we tried to establish a system for a systematic, comprehensive, and whole-brain tractography well beyond the initial 11 tracts. Rather than developing a specific TRS set for every tract, we used our DTI-based human brain atlas (Oishi et al., 2009
), in which 130 gray and white matter areas are pre-segmented. Second, the study focused on those tracts that had been difficult to systematically reconstruct with manual ROIs, such as the thalamic radiation and short association tracts. Third, for accurate mapping of the TRS to each subject, dual-contrast Large Deformation Diffeomorphic Metric Mapping (LDDMM) (Ceritoglu et al., 2009
) was employed. Fourth, we created probabilistic maps of a comprehensive set of tracts, which were built into our atlas for automated tract-specific analyses. In this paper, reconstructions of 59 tracts are reported and validated through comparison with the manual method, and the cross-subject reproducibility was measured. The newly developed tools are now incorporated into our widely available MRIstudio/RoiEditor software (www.mristudio.org