Abstract
Magnetic Resonance Imaging is a common way of diagnosing related diseases. However, the magnetic resonance images are easily defected by motion artifacts in their acquisition process, which severely affects the clinicians’ diagnosis. To resolve the problem, we propose a motion correction network (MocNet) to correct motion artifacts. The experiments of motion artifacts simulation demonstrate that our MocNet outperforms the comparison methods with a mean PSNR of 34.397 ± 3.155 dB and a mean SSIM of 0.971 ± 0.015.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (61871239, 62076077) and the Natural Science Foundation of Tianjin (20JCQNJC0125).
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Zhao, B. et al. (2022). MocNet: Less Motion Artifacts, More Clean MRI. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_6
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DOI: https://doi.org/10.1007/978-981-16-9423-3_6
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