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[Deep learning method for magnetic resonance imaging fluid-attenuated inversion recovery image synthesis]

Jianing Zhou1, Hongyu Guo1,2, Hong Chen1

  • 1School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|October 25, 2023
PubMed
Summary

This study introduces a deep learning network for synthesizing high-quality Magnetic Resonance Imaging (MRI) fluid-attenuated inversion recovery (FLAIR) images. This method can reduce patient scanning time and address issues with missing or low-quality FLAIR scans.

Keywords:
Deep learningFeature fusionMagnetic resonance imagingMulti-modal

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