Wang Lei, Beg Faisal, Ratnanather Tilak, Ceritoglu Can, Younes Laurent, Morris John C, Csernansky John G, Miller Michael I
Department of Psychiatry, Washington University School of Medicine, Campus Box 8134, 660 S. Euclid Ave, St. Louis, MO 63110, USA.
IEEE Trans Med Imaging. 2007 Apr;26(4):462-70. doi: 10.1109/TMI.2005.853923.
In large-deformation diffeomorphic metric mapping (LDDMM), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. Thus, methods such as principal component analysis (PCA) of the initial momentum leads to analysis of anatomical shape and form in target images without being restricted to small-deformation assumption in the analysis of linear displacements. We apply this approach to a study of dementia of the Alzheimer type (DAT). The left hippocampus in the DAT group shows significant shape abnormality while the right hippocampus shows similar pattern of abnormality. Further, PCA of the initial momentum leads to correct classification of 12 out of 18 DAT subjects and 22 out of 26 control subjects.
在大变形微分同胚度量映射(LDDMM)中,图像的微分同胚匹配被建模为时间演化,或由控制演化的相关平滑速度向量场v产生的流。初始动量参数化了整个测地线,并编码了目标图像的形状和形态。因此,诸如对初始动量进行主成分分析(PCA)的方法能够在不局限于线性位移分析中的小变形假设的情况下,对目标图像中的解剖形状和形态进行分析。我们将此方法应用于阿尔茨海默病型痴呆(DAT)的研究。DAT组的左侧海马体显示出明显的形状异常,而右侧海马体也呈现出类似的异常模式。此外,对初始动量进行PCA能够正确分类18名DAT受试者中的12名以及26名对照受试者中的22名。