Unsupervised MRI Super-Resolution Reconstruction Using a Hybrid Regularizer Powered Deep Image Prior

Motivation: While deep networks have shown great effectiveness for post-acquisition MRI resolution enhancement, their training requires an enormous of datasets. Deep Image Prior (DIP) is a novel approach that leverages the inductive bias of deep convolutional architecture, allowing for MRI super-resolution without the need for training. Goal(s): We aim to improve the capabilities of DIP and thus achieve resolution enhancement. Approach: We introduced a hybrid regularizer that integrates total variation with a neural network denoiser into the DIP framework. Results: Validated on 5T MR datasets, our method further improved on DIP and generated high-resolution MRI with realistic details, outshining several competing methods. Impact: The proposed unsupervised method offered a robust framework for MRI super-resolution reconstruction that leverages intrinsic image structure to ensure resolution enhancement without the need for training data, thus boosting the efficiency of medical imaging and potentially benefiting clinical diagnostics.