Khan Ali R, Beg Mirza Faisal
School of Engineering Science, Faculty of Applied Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5797-800. doi: 10.1109/IEMBS.2009.5335196.
We present here a novel method for whole brain magnetic resonance (MR) image registration that explicitly penalizes the mismatch of cortical and subcortical regions by simultaneously utilizing anatomic segmentation information from multiple cortical and subcortical structures, represented as volumetric images, with given T1-weighted MR image for registration. The registration is computed via variational optimization in the space of smooth velocity fields in the large deformation diffeomorphic metric matching (LDDMM) framework. We tested our method using a set of 10 manually labeled brains, and found quantitatively that subcortical and cortical alignment is improved over traditional single-channel MRI registration. We use this new method to generate a volumetric and cortical surface-based population average. The average grayscale image is found to be crisp, and allows the reconstruction and labeling of the cortical surface.
我们在此介绍一种全新的全脑磁共振（MR）图像配准方法，该方法通过同时利用来自多个皮质和皮质下结构的解剖分割信息（表示为体积图像）与给定的T1加权MR图像进行配准，明确地惩罚皮质和皮质下区域的不匹配。配准是在大变形微分同胚度量匹配（LDDMM）框架下，通过在平滑速度场空间中进行变分优化来计算的。我们使用一组10个手动标记的大脑对我们的方法进行了测试，并定量发现与传统单通道MRI配准相比，皮质下和皮质对齐得到了改善。我们使用这种新方法生成基于体积和皮质表面的群体平均值。发现平均灰度图像清晰，并且允许对皮质表面进行重建和标记。
