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A comparative study on the enhancement of high resolution coronal image quality by deep learning reconstruction
YANG Jing  LI Qiongge  WU Tao  QI Zhigang  ZHAO Cheng  LU Jie 


[Abstract] Objective To investigate the role of deep learning reconstruction (DLR) in improving image quality of high resolution T2 fluid-attenuated inversion-recovery sequency (FLAIR) coronal magnetic resonance imaging of hippocampus.Materials and Methods A total of 36 patients were prospectively enrolled in this study. GE Singna Premier 3.0 T MR scanner was used to perform high resolution T2-FLAIR coronal scanning of the hippocampus. Two diagnosticians assessed the image noise, artifacts, hippocampal structure, lesion, and diagnostic acceptance of origin reconstruction (OR) T2-FLAIR and DLR T2-FLAIR coronal images, respectively. Two technologists measured and calculated the signal to noise ratio (SNR), contrast noise ratio (CNR), and the difference in bilateral hippocampal signal intensity between the two groups of images.Results The noise, hippocampal structure identification, lesion identification and diagnostic acceptance score from DLR based coronal T2-FLAIR hippocampal image were higher than OR T2-FLAIR hippocampal coronal image (P<0.001), and there was no significant difference in artifacts evaluation (Z=-1.730; P=0.084). The SNR and CNR values of DLR T2-FLAIR hippocampal coronal images were significantly higher than those of OR T2-FLAIR hippocampal coronal images (t=-13.061, P<0.001; t=16.224; P<0.001). There was no significant difference in the difference of bilateral hippocampal signal between the two groups (t=-0.290; P=0.977).Conclusions Without prolonging the scanning time, Deep learning reconstruction can significantly improve the resolution of hippocampal structures and small lesions in high resolution T2-FLAIR coronal images. It can reduce noise, and provide high quality images for clinical use.
[Keywords] hippocampus;deep learning reconstruction;epilepsy;signal to noise ratio;contrast noise ratio;magnetic resonance imaging

YANG Jing1, 2   LI Qiongge1, 2   WU Tao3   QI Zhigang1, 2   ZHAO Cheng1, 2   LU Jie1, 2*  

1 Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China

2 Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100053, China

3 Clinical Marketing Department of MR, General Electric Medical (China) Co., Ltd., Beijing 100176, China

Corresponding author: Lu J, E-mail:

Conflicts of interest   None.

Received  2022-07-02
Accepted  2022-10-12
DOI: 10.12015/issn.1674-8034.2023.05.005

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