The ICBM-152 space is one of the most popular coordinate systems in the neuroimaging community. Several diffusion templates have been developed in the ICBM-152 space, including diffusion tensor imaging (DTI) templates^1-3, high angular resolution diffusion imaging (HARDI) templates^4,5, and a diffusion spectrum imaging (DSI) template⁶. For each of these templates, diffusion data of a large amount of healthy subjects were registered altogether and averaged to produce the final template. With the advance in imaging technology and registration algorithm, new diffusion templates will emerge to reveal more detailed white matter (WM) structures than the conventional clinically-feasible diffusion data. In the present study, therefore, we used a set of high quality diffusion data from the human connectome project (HCP)⁷ to build a diffusion template in the ICBM-152 space. Specifically, we used the strategy suggested by Hsu et al.⁶ to build the HCP diffusion template, as this registration strategy has been demonstrated to achieve better alignment in anatomical features. In addition, the HCP diffusion template was saved in the raw diffusion-weighted image (DWI) format, allowing various possible post-processing approaches.

The HCP database comprised more than 500 subjects, wherein the diffusion data of $$$N=489$$$ subjects were available. Each of the HCP diffusion data was acquired using a 3-shell q-space sampling scheme, 90 points per shell, where the b-values of the shells were 1000, 2000, and 3000 (s/mm²). The spatial resolution of the DWI was 1.25 mm³. The HCP diffusion data were preprocessed⁸ to correct for the eddy current-induced distortions, susceptibility-induced distortions, and motion-related artifacts, and then rigidly registered to the T1w images. With the preprocessed data, the procedures of building the HCP diffusion template were as follow.

(1) The individual T1w and T2w images were segmented⁹ through SPM12, resulting in the individual tissue probability maps (TPMs).

(2) The Shoot¹⁰ toolbox of SPM12 was employed to estimate the Karcher mean of the TPMs and the associated deformation maps, $$$Φ^j$$$, $$$j=1...N$$$.

(3) The DARTEL¹¹ toolbox of SPM12 was used to normalize the mean TPM to the ICBM-152 template, resulting in the deformation map, $$$Φ^0$$$.

(4) Combining the results of steps (2) and (3), produced the deformation maps $$$Θ^j=Φ^j\circΦ^0$$$, $$$j=1...N$$$, which mapped the individual spaces the ICBM-152 space.

(5) The individual diffusion data were transformed to the ICBM-152 space according to $$$Θ^j$$$, $$$j=1...N$$$, where the q-space signals of each voxel were reoriented through the local rotation matrixes. For the image space signals, the 2^nd order b-spline interpolation was used; for the q-space signals, the 4^th order spherical harmonic interpolation was performed for each q-shell.

(6) The transformed diffusion data were then averaged to produce the final HCP diffusion template.

The final HCP diffusion template consisted of 271 DWI at spatial resolution of 0.7 mm³, corresponding to the 3 q-shells in the q-space. Figure 1 displays the color-coded general fractional anisotropy (GFA) map on top of the ICBM-152 T1w image, where regions with GFA>0.04 were shown. In the deep WM regions, the orientations coincide with the known anatomy, and in the superficial WM regions, the WM contours spatially conform to the gyrus-sulcus patterns of the ICBM-152 template, suggesting that the HCP diffusion template matches well to the ICBM-152 space. Figure 2 shows the GFA map at the boundaries between the gray matter (GM) and WM. It reveals the cortical-depth dependence of the GFA values¹². More specifically, the cerebral cortex can be divided into two layers, the one close to the GW-WM boundary presents lower GFA values, leading to dark bands in the cortical regions, such as those indicated by the red arrowheads. Figure 3 illustrates the U-shape tracts which connected the right precentral and caudal-middle-frontal gyri, demonstrating the capability of the HCP diffusion template to estimate the small fibers.The present study uses the HCP diffusion data to develop a diffusion template in the ICBM-152 space. Thanks to the high quality HCP diffusion data and the state-of-the-art registration strategy, the HCP diffusion template is capable of revealing detailed diffusion-dependent structures. This template can be used to build a more comprehensive tract atlas, particularly the tracts that are very difficult to be reconstructed, such as the U-shape tracts, the tracts passing through the brain stem, and the cerebellar tracts. Another potential application of the template is tract-based parcellations for, say, thalamic nuclei and hippocampi subfields.