Keywords: Sparse & Low-Rank Models, Velocity & Flow
Motivation: Conventional 4D flow MRI provides valuable insights into blood flow but suffers from long scan times. Recent machine learning methods improve MRI reconstruction; however, they often require a large amount of training data to achieve desired performance.
Goal(s): This work is aimed to introduce a novel learning-based image reconstruction method to accelerate 4D flow MRI without using training datasets.
Approach: The proposed method integrates low-rank modelling with a deep generative prior by utilizing an untrained generative neural network to represent the spatial subspace of the model.
Results: The effectiveness of the proposed method has been demonstrated with in-vivo aortic 4D flow experiments.
Impact: This work introduced an innovative learning-based image reconstruction method for accelerating 4D flow MRI, which produces accurate velocity measurements even under high acceleration factor, all without the need for training datasets.
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