Chen Hua-Mei, Goela Aashish, Garvin Gregory J, Li Shuo
Department of Medical Biophysics, UWO, London, Ontario.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):340-8. doi: 10.1007/978-3-642-15705-9_42.
This study investigates a new parameterization of deformation fields for image registration. Instead of standard displacements, this parameterization describes a deformation field with its transformation Jacobian and curl of end velocity field. It has two important features which make it appealing to image registration: 1) it relaxes the need of an explicit regularization term and the corresponding ad hoc weight in the cost functional; 2) explicit constraints on transformation Jacobian such as topology preserving and incompressibility constraints are straightforward to impose in a unified framework. In addition, this parameterization naturally describes a deformation field in terms of radial and rotational components, making it especially suited for processing cardiac data. We formulate diffeomorphic image registration as a constrained optimization problem which we solve with a step-then-correct strategy. The effectiveness of the algorithm is demonstrated with several examples and a comprehensive evaluation of myocardial delineation over 120 short-axis cardiac cine MRIs acquired from 20 subjects. It shows competitive performance in comparison to two recent segmentation based approaches.
本研究探讨了一种用于图像配准的变形场新参数化方法。该参数化方法不是用标准位移，而是用变换雅可比行列式和末速度场的旋度来描述变形场。它有两个重要特性，使其在图像配准中颇具吸引力：1）它放宽了对显式正则化项以及代价函数中相应临时权重的需求；2）对变换雅可比行列式的显式约束，如拓扑保持和不可压缩性约束，在统一框架中易于施加。此外，这种参数化方法自然地根据径向和旋转分量描述变形场，使其特别适合处理心脏数据。我们将微分同胚图像配准公式化为一个约束优化问题，并采用先逐步求解再校正的策略来解决。通过几个例子展示了该算法的有效性，并对从20名受试者获取的120幅短轴心脏电影磁共振成像进行了心肌轮廓的综合评估。与最近的两种基于分割的方法相比，它表现出了有竞争力的性能。
