Objective.Deformable registration plays a crucial role in motion estimation from a sequence of cardiac magnetic resonance (CMR) imaging, which is good for the diagnosis and treatment of heart diseases. To address the challenges posed by intensity inhomogeneity and complex deformation, we propose a novel convolutional neural network-Transformer framework for this task.Approach.In this study, a convolutional projection Transformer block that enables efficient self-attention computation was designed for modeling long-range spatial correspondences. Additionally, a cooperative learning pattern was adopted for fusing information from global and local features. Finally, multi-resolution strategy was employed for optimizing model parameters in coarse-to-fine manner.Main result.The proposed method was evaluated on three different CMR datasets for intra-subject registration. Experimental results show that the proposed method achieves better Dice overlap and lower surface distance, compared to four non-learning-based methods and three deep-learning-based methods.Significance.For the challenging task of CMR image registration, our method demonstrates superior performance, delivering more accurate results with lower complexity. It may thus facilitate cardiac motion estimation for clinical assessments of cardiac function.
Keywords: cardiac magnetic resonance; deformable registration; motion estimation; multi-resolution optimization; vision Transformer.
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