Mangin J F, Frouin V, Bloch I, Bendriem B, Lopez-Krahe J
Service Hospitalier Frédéric Joliot, CEA, Orsay, France.
J Cereb Blood Flow Metab. 1994 Sep;14(5):749-62. doi: 10.1038/jcbfm.1994.96.
We propose a fully nonsupervised methodology dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically extracted from both images. Then, a shape-independent surface-matching algorithm gives a rigid body transformation, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic generalized distance between discrete surfaces, taking into account between-modality differences in the localization of the segmented surfaces. The minimization process is efficiently performed via the precomputation of a 3D distance map. Validation studies using a dedicated brain-shaped phantom have shown that the maximum registration error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The software is routinely used today in a clinical context by the physicians of the Service Hospitalier Frédéric Joliot (> 150 registrations performed). The entire registration process requires approximately 5 min on a conventional workstation.
我们提出了一种完全无监督的方法，专门用于快速配准大脑的正电子发射断层扫描（PET）和磁共振图像。首先，从这两种图像中自动提取感兴趣表面（头部或大脑表面）的离散表示。然后，一种与形状无关的表面匹配算法给出刚体变换，这允许在两种模态之间传递信息。倒角匹配原理的三维（3D）扩展构成了这种表面匹配算法的核心。从离散表面之间二次广义距离的最小化推断出最优变换，同时考虑到分割表面定位中的模态间差异。通过预先计算三维距离图有效地执行最小化过程。使用专用脑形体模的验证研究表明，对于各种测试配置，最大配准误差约为PET像素大小（2毫米）。该软件如今在弗雷德里克·约里奥医院的医生临床工作中经常使用（已进行超过150次配准）。在传统工作站上，整个配准过程大约需要5分钟。
