Accelerated Magnetic Resonance Imaging via Quantum Computing and Deep Neural Network
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.
Authors
- Yihang Zhou (ORCID: https://orcid.org/0000-0001-6354-1259)
- Haifeng Wang (ORCID: https://orcid.org/0000-0003-4229-3668)
- Hairong Zheng (ORCID: https://orcid.org/0000-0002-8558-5102)
- Yanjie Zhu (ORCID: https://orcid.org/0000-0002-9131-689X)
- Congcong Liu (ORCID: https://orcid.org/0000-0003-3949-4194)
- Shuo Zhou (ORCID: https://orcid.org/0000-0002-6475-3293)
- Dong Liang (ORCID: https://orcid.org/0000-0001-6257-0875)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/5125
- Primary Topic
- Atomic and Subatomic Physics Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00