GE Global Research, Niskayuna, NY 12309, USA. e-mail:
IEEE Trans Med Imaging. 2012 Mar;31(3):749-65. doi: 10.1109/TMI.2011.2178609. Epub 2011 Dec 20.
This paper presents registration via embedded maps (REM), a deformable registration algorithm for images with varying topology. The algorithm represents 3-D images as 4-D manifolds in a Riemannian space (referred to as embedded maps). Registration is performed as a surface evolution matching one embedded map to another using a diffusion process. The approach differs from those existing in that it takes an a priori estimation of image regions where topological changes are present, for example lesions, and generates a dense vector field representing both the shape and intensity changes necessary to match the images. The algorithm outputs both a diffeomorphic deformation field and an intensity displacement which corrects the intensity difference caused by topological changes. Multiple sets of experiments are conducted on magnetic resonance imaging (MRI) with lesions from OASIS and ADNI datasets. These images are registered to either a brain template or images of healthy individuals. An exemplar case registering a template to an MRI with tumor is also given. The resulting deformation fields were compared with those obtained using diffeomorphic demons, where topological changes are not modeled. These sets of experiments demonstrate the efficacy of our proposed REM method for registration of brain MRI with severe topological differences.
本文提出了基于嵌入式图谱的配准方法（REM），这是一种针对拓扑变化的图像的可变形配准算法。该算法将 3D 图像表示为黎曼空间中的 4D 流形（称为嵌入式图谱）。配准是通过扩散过程将一个嵌入式图谱匹配到另一个嵌入式图谱来实现的。该方法与现有方法的不同之处在于，它对存在拓扑变化的图像区域（例如病变）进行了先验估计，并生成了一个密集的向量场，该向量场表示匹配图像所需的形状和强度变化。该算法输出的不仅是一个可微分的变形场，还有一个强度位移，它可以纠正由拓扑变化引起的强度差异。我们在 OASIS 和 ADNI 数据集的病变磁共振成像（MRI）上进行了多组实验。这些图像被注册到大脑模板或健康个体的图像上。还给出了一个将模板注册到带有肿瘤的 MRI 的示例案例。将得到的变形场与使用不考虑拓扑变化的可微分恶魔算法得到的变形场进行了比较。这些实验结果表明，我们提出的 REM 方法对于具有严重拓扑差异的大脑 MRI 配准是有效的。
