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Abstract #0346

Unsupervised 4D-Flow MRI reconstruction with Deep Image Prior and Graph Convolution Neural-Network

Zhongsen Li1, Aiqi Sun2, Wenxuan Chen1, Xiancong Liu1, Haining Wei1, Chuyu Liu1, and Rui Li1
1Tsinghua University, Beijing, China, 2Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States

Synopsis

Keywords: AI/ML Image Reconstruction, Cardiovascular

Motivation: Deep learning reconstruction algorithms offer significant advantages for accelerating 4D-Flow MRI acquisition. However, a large high-quality fully-sampled dataset is usually unavailable for network training.

Goal(s): To propose an unsupervised algorithm for 4D-Flow MRI reconstruction, without the need for any fully-sampled data.

Approach: We use branched CNNs and a Graph-Convolution-Network as the generator. Additionally, we devise an ADMM algorithm to alternately optimize the images and the network parameters. Experiments are conducted on aortic and intracranial 4D-Flow data.

Results: The proposed algorithm demonstrates superior reconstruction results, outperforming even supervised deep-learning method. Moreover, it exhibits good generalization capability when applied to another imaging target.

Impact: The proposed method is a promising algorithm for accelerating MR blood-flow imaging, owing to its exceptional performance and generalization capacity. Furthermore, the algorithm introduces a new model for 4D-flow MRI reconstruction which is valuable for further research.

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Keywords