Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, MRI, diffusion modelsHigh-resolution MR images are advantageous for medical diagnosis. However, they may require longer scan time and more powerful hardware. Significant effort has been made to carry out super-resolution methods to synthesize higher-resolution MR images from lower-resolution acquisitions using deep learning approaches. However, most current methods are biased. In this work, we propose a new super-resolution method based on the state-of-the-art generative model (i.e., variational diffusion model), using the lower-resolution K-space measurements as a condition for guiding the super-resolution process. This unsupervised approach achieved high performance for reconstructing MR images from arbitrary lower resolutions without retraining the model.
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