Keywords: Image Reconstruction, AI/ML Image Reconstruction
Motivation: Applying quantum computing to the reconstruction of magnetic resonance imaging.
Goal(s): Improving the quality of magnetic resonance reconstruction images by introducing quantum computing.
Approach: Building a quantum hybrid classical neural network for undersampling magnetic resonance reconstruction.
Results: The hybrid classical neural network achieved better accuracy by introducing quantum convolutional layers and verified the feasibility of applying quantum computing to magnetic resonance reconstruction.
Impact: This fully demonstrates the potential and broad development prospects of quantum computing in accelerating magnetic resonance imaging.
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