Unsupervised learning-based registration for T1 and T2 breast MRI images

The traditional image registration methods use the iterative optimization to search the most optimal transformation parameter. However, they are all time-consuming. Therefore, we proposed a fast and unsupervised T1/T2 weighted breast images registration model based on the convolutional neural network. The proposed model estimate the spatial transformations from pairs of T1/T2 weighted breast images , and used the transformations to warp the moving images. There is no supervised information such as the ground truth of the deformation fields should be provided in this model. The registration of T1/T2 weighted breast MRI images can assist doctors to get better diagnosis results and provide better data resource for lesion detection and image prediction. We have evaluated the proposed model and the experimental results show that the model is robust and effective.