Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, MRI reconstruction, self-supervised, deep learning Although Deep learning (DL) techniques are powerful for MRI reconstruction, their development is hindered by the need for large training datasets. We propose a self-supervised method for training DL reconstruction networks using only limited-resolution data, acquired in k-space “bands”, which are generally easier to acquire than variable-density full-resolution data. Although the network is trained using low-resolution data, during inference it can reconstruct high-resolution images. Comprehensive experiments demonstrate that k-band is robust to various acceleration factors, outperforms two other methods trained on low-resolution data, and obtains comparable performance with SSDU and MoDL, while also reducing the need for full-resolution data.
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