We present an unsupervised deep image registration framework for MR image reconstruction. Specifically, even and odd echo images from fast spin echo-based sequence are nonlinearly registered using a convolutional network that estimates the deformation field. The registered echo images are then averaged for noise reduction. The proposed framework was evaluated across four imaging contrasts (T1w, T2w, FLAIR, and DWI) from a low-field MR scanner and was found to outperform nonlinear registration from advanced normalization tools, yielding sharper image quality and preserving important pathology features.
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