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Optical Flow based Deformable Volume Registration using a novel Second-Order Regularization Prior

机译:使用新型二阶正则化先验的基于光流的可变形体配准

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Nonlinear image registration is an initial step for a large number of medical image analysis applications. Optical flow based intensity registration is often used for dealing with intra-modality applications involving motion differences. In this work we present an energy functional which uses a novel, second-order regularization prior of the displacement field. Compared to other methods our scheme is robust to non-Gaussian noise and does not penalize locally affine deformation fields in homogeneous areas. We propose an efficient and stable numerical scheme to find the minimizer of the presented energy. We implemented our algorithm using modern consumer graphics processing units and thereby increased the execution performance dramatically. We further show experimental evaluations on clinical CT thorax data sets at different breathing states and on dynamic 4D CT cardiac data sets.
机译:非线性图像配准是许多医学图像分析应用程序的第一步。基于光流的强度配准通常用于处理涉及运动差异的模态内应用。在这项工作中,我们提出了一种能量函数,该函数在位移场之前使用了新颖的二阶正则化。与其他方法相比,我们的方案对非高斯噪声具有鲁棒性,并且不会惩罚均匀区域中的局部仿射变形场。我们提出了一种有效且稳定的数值方案来找到所提出能量的最小化器。我们使用现代的消费者图形处理单元实现了算法,从而大大提高了执行性能。我们进一步显示了在不同呼吸状态下的临床CT胸部数据集和动态4D CT心脏数据集的实验评估。

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