Risser Laurent, Vialard François-Xavier, Wolz Robin, Holm Darryl D, Rueckert Daniel
Institute for Mathematical Science, Imperial College London, 53 Prince's Gate, SW7 2PG London, UK.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):610-7. doi: 10.1007/978-3-642-15745-5_75.
In this paper, we present a fine and coarse approach for the multiscale registration of 3D medical images using Large Deformation Diffeomorphic Metric Mapping (LDDMM). This approach has particularly interesting properties since it estimates large, smooth and invertible optimal deformations having a rich descriptive power for the quantification of temporal changes in the images. First, we show the importance of the smoothing kernel and its influence on the final solution. We then propose a new strategy for the spatial regularization of the deformations, which uses simultaneously fine and coarse smoothing kernels. We have evaluated the approach on both 2D synthetic images as well as on 3D MR longitudinal images out of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Results highlight the regularizing properties of our approach for the registration of complex shapes. More importantly, the results also demonstrate its ability to measure shape variations at several scales simultaneously while keeping the desirable properties of LDDMM. This opens new perspectives for clinical applications.
在本文中，我们提出了一种使用大变形微分同胚度量映射（LDDMM）对3D医学图像进行多尺度配准的精细和粗略方法。这种方法具有特别有趣的特性，因为它能估计出大的、平滑且可逆的最优变形，这些变形对于量化图像中的时间变化具有丰富的描述能力。首先，我们展示了平滑核的重要性及其对最终解的影响。然后，我们提出了一种用于变形空间正则化的新策略，该策略同时使用精细和平滑核。我们已经在二维合成图像以及阿尔茨海默病神经成像倡议（ADNI）研究中的三维磁共振纵向图像上对该方法进行了评估。结果突出了我们的方法在复杂形状配准方面的正则化特性。更重要的是，结果还证明了它能够同时在多个尺度上测量形状变化，同时保持LDDMM的理想特性。这为临床应用开辟了新的前景。
