Abstract
Registration of hepatic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series remains challenging, due to variable uptakes of the agent on different tissues or even the same tissues in the liver. The differences reflect on the intensity variations, which typically makes traditional intensity-based deformable registration methods fail to align small anatomical structures in liver such as vessels. Although deep-learning-based registration methods have become popular because of their superior efficiency for several years, registration of DCE-MRI series with dynamic intensity change is still under tackle. To solve this challenge, we present a two-stage registration network, in which the first stage aligns the whole liver and the second stage focuses on the registration of anatomical structures like vessels and tumors. Furthermore, we adopt a recurrent registration strategy for the deformation refinement. To evaluate our proposed method, we used clinical DCE-MRI series of 60 patients, and registered the arterial phase and the portal venous phase images onto the pre-contrast phases. Experimental results showed that the proposed method achieved a better registration performance than the traditional method (i.e., SyN) and the deep-learning-based method (i.e., VoxelMorph), especially in aligning anatomical structures such as vessel branches in liver.
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This study has received funding by the National Natural Science Foundation of China (No. 91859107).
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Shen, W. et al. (2021). A Recurrent Two-Stage Anatomy-Guided Network for Registration of Liver DCE-MRI. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_23
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