Saghafi Behrouz, Chen Geng, Shi Feng, Yap Pew-Thian, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA.
Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA; Data Processing Center, Northwestern Polytechnical University, Xi'an, China.
Patch Based Tech Med Imaging (2016). 2016 Oct;9993:9-16. doi: 10.1007/978-3-319-47118-1_2. Epub 2016 Sep 22.
Atlases constructed using diffusion-weighted imaging (DWI) are important tools for studying human brain development. Atlas construction is in general a two-step process involving image registration and image fusion. The focus of most studies so far has been on improving registration thus image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this paper, we propose a patch-based method for DWI atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free. Instead of generating an atlas for each gradient direction independently and hence neglecting inter-image correlation, we propose to construct the atlas by jointly considering diffusion-weighted 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 verify that our atlas, constructed for neonatal data, reveals more structural details compared with the average atlas especially in the cortical regions. Our atlas also yields greater accuracy when used for image normalization.
使用扩散加权成像（DWI）构建的图谱是研究人类大脑发育的重要工具。图谱构建通常是一个两步过程，包括图像配准和图像融合。迄今为止，大多数研究的重点都在改进配准上，因此图像融合通常采用简单平均法进行，这常常导致图谱模糊不清。在本文中，我们提出了一种基于补丁的DWI图谱构建方法。与其他基于扩散张量模型的图谱不同，我们的图谱是无模型的。我们不是为每个梯度方向独立生成一个图谱，从而忽略图像间的相关性，而是建议通过联合考虑相邻梯度方向的扩散加权图像来构建图谱。我们采用了一个组正则化框架，其中对角度相邻图像的局部补丁进行约束，以实现一致的时空角度图谱重建。实验结果表明，我们为新生儿数据构建的图谱与平均图谱相比，揭示了更多的结构细节，尤其是在皮质区域。当用于图像归一化时，我们的图谱也具有更高的准确性。
