Afzali Maryam, Fatemizadeh Emad, Soltanian-Zadeh Hamid
Department of Electrical Engineering, Biomedical Signal and Image Processing Laboratory (BiSIPL), Sharif University of Technology, Tehran, Iran.
Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan, USA.
Comput Methods Programs Biomed. 2017 Nov;151:33-43. doi: 10.1016/j.cmpb.2017.08.003. Epub 2017 Aug 8.
Registration is a critical step in group analysis of diffusion weighted images (DWI). Image registration is also necessary for construction of white matter atlases that can be used to identify white matter changes. A challenge in the registration of DWI is that the orientation of the fiber bundles should be considered in the process, making their registration more challenging than that of the scalar images. Most of the current registration methods use a model of diffusion profile, limiting the method to the used model.
We propose a model-independent method for DWI registration. The proposed method uses a multi-level free-form deformation (FFD), a sparse similarity measure, and a dictionary. We also propose a synthesis K-SVD algorithm for sparse interpolation of images during the registration process. We utilize two dictionaries: analysis dictionary is learned based on diffusion signals while synthesis dictionary is generated based on image patches. The proposed multi-level approach registers anatomical structures at different scales. T-test is used to determine the significance of the differences between different methods.
We have shown the efficiency of the proposed approach using real data. The method results in smaller generalized fractional anisotropy (GFA) root mean square (RMS) error (0.05 improvements, p = 0.0237) and angular error (0.37 ° improvement, p = 0.0330) compared to the large deformation diffeomorphic metric mapping (LDDMM) method and advanced normalization tools (ANTs).
Sparse registration of diffusion signals enables registration of diffusion weighted images without using a diffusion model.
配准是扩散加权成像（DWI）组分析中的关键步骤。图像配准对于构建可用于识别白质变化的白质图谱也是必要的。DWI配准中的一个挑战是在配准过程中应考虑纤维束的方向，这使得其配准比标量图像的配准更具挑战性。当前大多数配准方法使用扩散轮廓模型，这限制了该方法仅适用于所使用的模型。
我们提出一种用于DWI配准的与模型无关的方法。该方法使用多级自由形式变形（FFD）、稀疏相似性度量和一个字典。我们还提出一种合成K-SVD算法，用于在配准过程中对图像进行稀疏插值。我们使用两个字典：分析字典基于扩散信号学习，而合成字典基于图像块生成。所提出的多级方法在不同尺度上配准解剖结构。使用t检验来确定不同方法之间差异的显著性。
我们使用真实数据证明了所提出方法的有效性。与大变形微分同胚度量映射（LDDMM）方法和高级归一化工具（ANTs）相比，该方法导致更小的广义分数各向异性（GFA）均方根（RMS）误差（提高了0.05，p = 0.0237）和角度误差（提高了0.37°，p = 0.0330）。
扩散信号的稀疏配准能够在不使用扩散模型的情况下对扩散加权图像进行配准。
