Robust Groupwise Affine Registration of Medical Images with Stochastic Optimization
Robust registration of medical images with missing correspondences caused by pathological structures or anatomical variations is still a challenging problem. This paper presents a robust method for groupwise affine registration based on the RASL algorithm  that formulates the registration problem as a sparse and low-rank decomposition. We adapt the RASL algorithm for the alignment of 3D image data and introduce a stochastic optimization scheme to enable the computational tractability. Further, a normalization scheme generates more plausible and unique transformations. In our experiments, the algorithm has been applied to various medical images, and proves its suitability for medical image registration. Especially, the approach shows advantages in the presence of pathologies and outperforms iterative groupwise registration based on ITK. The stochastic optimization scheme generates a significant acceleration allowing for a groupwise affine registration of ten 3D CT images in ∼ 5 minutes on CPU without elaborate optimization.
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