The concatenation of several-element U-nets operating in both k-space and image domains is a deep learning network model that has been used for magnetic resonance image (MRI) reconstruction. Here, we present a new method that incorporates deformable 2D convolution kernels into the model. The proposed method leverages motion information of dynamic MRI and thus deformable convolution kernel naturally adapts to image structures. We demonstrate the improved performance of the proposed method using CINE dataset.
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