Equivariant Deformable Convolutions for Unrolling Networks in Cardiac Cine MR Imaging

Deep unrolling methods have achieved notable success in accelerated cardiac cine MRI reconstruction. However, their effectiveness remains limited by constrained receptive fields and rigid convolutional sampling, which hinder scalability in high-resolution reconstruction tasks. To address these challenges, we propose an Equivariant Deformable Convolutional Unrolling Network (EDCU-Net) that integrates a Spatiotemporal Deformable Module (STDM) and a Rotation Equivariant Module (REM). EDCU-Net effectively enlarges the receptive field while maintaining low computational cost and high parameter efficiency, enabling more effective suppression of large-scale aliasing artifacts. Specifically, STDM adaptively adjusts sampling locations to better capture complex anatomical structures and dynamic cardiac motion, while reducing interpolation overhead. Furthermore, REM embeds deformable convolutions within an equivariant framework, allowing shared parameters across orientations and further promoting parameter efficiency and generalization capability. Extensive experiments on cardiac cine MRI data demonstrate that EDCU-Net consistently outperforms state-of-the-art methods in both reconstruction accuracy and visual quality.