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Stop moving: MR motion correction as an opportunity for artificial intelligence

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Abstract

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.

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Funding

Funding was provided by National Natural Science Foundation of China (grant no. 82102027, grant no. 82250610232, and grant no. 82302295) and National High-tech Research and Development Program (grant no. SQ2022YFC2400133).

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  1. Peng Hu and Haikun Qi contributed equally.

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  1. School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China

    Zijian Zhou, Peng Hu & Haikun Qi

  2. Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China

    Zijian Zhou, Peng Hu & Haikun Qi

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  1. Zijian Zhou
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Zhou, Z., Hu, P. & Qi, H. Stop moving: MR motion correction as an opportunity for artificial intelligence. Magn Reson Mater Phy (2024). https://doi.org/10.1007/s10334-023-01144-5

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