Institute for Mathematical Science, Imperial College, SW7 2PG, London, UK.
IEEE Trans Med Imaging. 2011 Oct;30(10):1746-59. doi: 10.1109/TMI.2011.2146787. Epub 2011 Apr 25.
In the framework of large deformation diffeomorphic metric mapping (LDDMM), we present a practical methodology to integrate prior knowledge about the registered shapes in the regularizing metric. Our goal is to perform rich anatomical shape comparisons from volumetric images with the mathematical properties offered by the LDDMM framework. We first present the notion of characteristic scale at which image features are deformed. We then propose a methodology to compare anatomical shape variations in a multi-scale fashion, i.e., at several characteristic scales simultaneously. In this context, we propose a strategy to quantitatively measure the feature differences observed at each characteristic scale separately. After describing our methodology, we illustrate the performance of the method on phantom data. We then compare the ability of our method to segregate a group of subjects having Alzheimer's disease and a group of controls with a classical coarse to fine approach, on standard 3D MR longitudinal brain images. We finally apply the approach to quantify the anatomical development of the human brain from 3D MR longitudinal images of pre-term babies. Results show that our method registers accurately volumetric images containing feature differences at several scales simultaneously with smooth deformations.
在大变形微分同胚度量映射（LDDMM）的框架下，我们提出了一种实用的方法，将关于已注册形状的先验知识集成到正则化度量中。我们的目标是从体积图像中利用 LDDMM 框架提供的数学性质进行丰富的解剖形状比较。我们首先介绍了在何种特征尺度下图像特征发生变形的特征尺度的概念。然后，我们提出了一种多尺度比较解剖形状变化的方法，即在几个特征尺度上同时进行比较。在这种情况下，我们提出了一种策略，可以分别定量测量每个特征尺度上观察到的特征差异。在描述了我们的方法之后，我们用幻影数据来说明该方法的性能。然后，我们将我们的方法与经典的粗到精方法比较，以分离一组患有阿尔茨海默病的患者和一组对照组的能力，用于标准的 3D MR 纵向脑图像。最后，我们应用该方法从早产儿的 3D MR 纵向图像中定量分析人类大脑的解剖发育。结果表明，我们的方法可以准确地注册包含几个尺度上的特征差异的体积图像，同时具有平滑的变形。
