Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, regularization method, interpretability, adversarial training
Motivation: Introduce latent optimization techniques to enhance the interpretability of learnable regularization methods, thereby improving the performance of MRI acceleration reconstruction.
Goal(s): Theoretically, we aim to elucidate the iterative direction of learnable regularization methods. Experimentally, we aim to achieve high-quality reconstruction of undersampled MRI data.
Approach: Revise the optimization objective of the network by incorporating a stochastic gradient descent generator, training learnable regularizers that guide the latent process during iteration, and accomplish reconstruction using the projected gradient method.
Results: Compared to other regularization methods, proposed method achieved a higher level of interpretability and accomplished higher-quality reconstruction.
Impact: The method directly learns the distribution information of real data and guides the iteration towards the real data manifold. We believe that the method and its theoretical properties are undoubtedly inspiring for researchers seeking to further acquire data distribution information.
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