Atlases constructed using diffusion-weighted imaging are important tools for studying human brain development. Atlas construction is in general a two- step process involving spatial registration and fusion of individual images. The focus of most studies so far has been on improving the accuracy of registration while image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this paper, we propose a patch-based method for diffusion-weighted (DW) atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free and generated directly from the diffusion-weighted images. Instead of independently generating an atlas for each gradient direction and hence neglecting angular image correlation, we propose to construct the atlas by jointly considering DW images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results confirm that our atlas, constructed for neonatal data, reveals more structural details with higher fractional anisotropy than the atlas generated without angular consistency as well as the average atlas. Also the normalization of test subjects to the proposed atlas results in better alignment of brain structures.
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