Commowick Olivier, Wiest-Daesslé Nicolas, Prima Sylvain
INRIA, INSERM, VisAGeS U746 Unit/Project, F-35042 Rennes, France.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):163-70. doi: 10.1007/978-3-642-33418-4_21.
We propose an iterative two-step method to compute a diffeomorphic non-rigid transformation between images of anatomical structures with rigid parts, without any user intervention or prior knowledge on the image intensities. First we compute spatially sparse, locally optimal rigid transformations between the two images using a new block matching strategy and an efficient numerical optimiser (BOBYQA). Then we derive a dense, regularised velocity field based on these local transformations using matrix logarithms and M-smoothing. These two steps are iterated until convergence and the final diffeomorphic transformation is defined as the exponential of the accumulated velocity field. We show our algorithm to outperform the state-of-the-art log-domain diffeomorphic demons method on dynamic cervical MRI data.
我们提出了一种迭代两步法，用于在具有刚性部分的解剖结构图像之间计算微分同胚非刚性变换，无需任何用户干预或关于图像强度的先验知识。首先，我们使用一种新的块匹配策略和高效的数值优化器（BOBYQA）计算两幅图像之间空间稀疏、局部最优的刚性变换。然后，我们基于这些局部变换，使用矩阵对数和M平滑导出一个密集的、正则化的速度场。这两个步骤反复迭代直至收敛，最终的微分同胚变换定义为累积速度场的指数。我们证明，在动态颈椎MRI数据上，我们的算法优于当前最先进的对数域微分同胚恶魔方法。
