Recent studies explored the merits of using T1-weighted image (T1WI) as a guidance to reconstruct other undersampled MRI modalities via convolutional neural networks (CNNs). However, even aided by T1WI, the reconstruction of highly undersampled MRI data is still suffering from aliasing artifacts, due to the one-upsampling style of existing CNN architectures and not fully using the T1 information. To address these issues, we propose a deep Laplacian pyramid MRI reconstruction framework (LapMRI), which performs progressive upsampling while integrating multiscale prior of T1WI. We show that LapMRI consistently outperforms state-of-the-art methods and can preserve anatomical structure faithfully up to 12-fold undersampling.
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