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Application of deep learning reconstruction in improving the quality of neuromelanin magnetic resonance image
YU Yang  ZHAO Cheng  QI Zhigang  WU Tao  LU Jie 

Cite this article as: YU Y, ZHAO C, QI Z G, et al. Application of deep learning reconstruction in improving the quality of neuromelanin magnetic resonance image[J]. Chin J Magn Reson Imaging, 2023, 14(5): 11-15. DOI:10.12015/issn.1674-8034.2023.05.003.


[Abstract] Objective To improve the image quality and shorten the scanning time of neuromelanin magnetic resonance imaging sequence commonly used in clinic by deep learning reconstruction (DL Recon).Materials and Methods A total of 30 volunteers were prospectively enrolled, and each volunteer was scanned with DL Recon 2D fast spin echo (FSE) T1WI sequence and clinical traditional 2D FSE T1WI. The original images of DL Recon 2D FSE T1WI were saved. After scanning, the three groups of images were evaluated subjectively and objectively. The "five point method" was used to score the image uniformity, sharpness, artifact and overall image quality, the results were statistically described by interquartile spacing[M (P25, P75)]. The objective evaluation was carried out from the signal to noise ratio (SNR) of substantia nigra (SC) and locus coeruleus (LC) of the midbrain and the contrast noise ratio (CNR) between the above areas and surrounding tissues. The results were statistically analyzed by ANOVA.Results The score of image evenness of DL Recon 2D FSE T1WI images, original images and clinical traditional 2D FSE T1WI images was 4 (4, 5), 4 (4, 5), 4 (4, 5) (Z=1.31, P＞0.05), the score of sharpness was 4 (4, 5), 3 (3, 4), 3 (3, 4) (Z=2.57, P＜0.001), the score of artifacts was 3 (3, 4), 4 (4, 5), 4 (4, 5) (Z=3.43, P＜0.001), and the score of overall image quality was 4 (4, 5), 3 (2, 3), 3 (3, 4) (Z=2.77, P＜0.001). In the subjective scores of the three groups of images, there was no significant difference between the three groups except image uniformity. There were significant differences in sharpness, artifacts and image quality in DL Recon 2D FSE T1WI group (P＜0.05). The objective evaluation results of DL Recon 2D FSE T1WI images, original images and clinical traditional 2D FSE T1WI images were as follows: SNRSN 250.38±9.02, 66.19±7.32, 110.91±10.10; SNRLC 220.41±12.02, 50.26±5.89, 90.38±11.70; CNRSN 25.30±3.42, 7.87±1.12, 8.01±1.38; CNRLC 30.17±2.23, 10.54±2.08, 11.11±1.89. The SNR and CNR of DL Recon 2D FSE T1WI group were higher than those of original image and traditional 2D FSE T1WI group, and the differences were statistically significant (P＜0.001).Conclusions DL Recon 2D FSE T1WI sequence can improve the signal to noise ratio and contrast to noise ratio of the original sequence image under the condition of ensuring the spatial resolution by using the deep learning noise reduction algorithm of the original K-space data, and can greatly shorten the scanning time. It is expected to become the mainstream means of neuromelanin magnetic resonance imaging.
[Keywords] substantia nigra；locus coeruleus；signal to noise ratio；contrast signal to noise ratio；deep learning reconstruction；magnetic resonance imaging

YU Yang1, 2   ZHAO Cheng1, 2   QI Zhigang1, 2   WU Tao3   LU Jie1, 2*  

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

2 Beijing Key Laboratory of Magnetic Resonance Imaging 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: imaginglu@hotmail.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Leading Talents Project from Huizhi Ascent Support Plan of Xuanwu Hospital (No. HZ2021ZCLJ005).
Received  2022-07-11
Accepted  2022-10-13
DOI: 10.12015/issn.1674-8034.2023.05.003
Cite this article as: YU Y, ZHAO C, QI Z G, et al. Application of deep learning reconstruction in improving the quality of neuromelanin magnetic resonance image[J]. Chin J Magn Reson Imaging, 2023, 14(5): 11-15. DOI:10.12015/issn.1674-8034.2023.05.003.

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