Accelerated 4D Flow MRI with Low-Rank Modeling and A Deep Generative Prior

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.