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• Technical Article •
Comparative use of artificial intelligence-assisted compressed sensing and parallel imaging for shoulder magnetic resonance imaging
YANG Zecheng  ZHAN Yi  SHI Nannan  SHANG Ai  SHAN Fei  SHEN Jie 

Cite this article as: YANG Z C, ZHAN Y, SHI N N, et al. Comparative use of artificial intelligence-assisted compressed sensing and parallel imaging for shoulder magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(8): 166-171. DOI:10.12015/issn.1674-8034.2024.08.025.


[Abstract] Objective By comparing with parallel imaging (PI), to explore the impact of artificial intelligence-assisted compressed sensing (ACS) technology on the scanning time and image quality of shoulder joint MRI, and optimizes the scanning scheme.Materials and Methods A total of 70 patients who underwent shoulder MRI in our hospital from November 2023 to February 2024 were prospectively enrolled. The scanning sequences used fast spin echo including oblique coronal T1-weighted (OCor T1WI), oblique coronal T2-weighted with fat saturation (OCor T2WI-fs), oblique sagittal proton density (PD)-weighted with fat saturation (OSag PDWI-fs), and transverse PD-weighted with fat saturation (Tra PDWI-fs), respectively, using two accelerated acquisition technologies: ACS and PI. Compare the scanning time of two technologies. Measure the signal intensity and background standard deviation of the supraspinatus muscle and humeral head, and calculate the signal-to-noise ratio (SNR). Use the Likert scale to rate image quality.Results Compared to PI, using ACS reduced scanning time by 33.5%. The images obtained using ACS have few artifacts and low noise. The subjective image quality scores are higher than those obtained using PI, and the differences are statistically significant (all P<0.05). The SNR of images using ACS in OCor T1WI, OCor T2WI-fs, and Tra PDWI-fs sequences were higher than those using PI in the supraspinatus muscle and humeral head, and the differences were statistically significant (all P<0.001). The SNR of the supraspinatus muscle in the OSag PDWI-fs sequence using ACS was not significantly different from that of PI (P>0.05), while the SNR of the humeral head in the images obtained using ACS was higher than that of PI, and the difference was statistically significant (all P<0.001).Conclusions Compared with PI, using ACS in shoulder MRI can achieve a more efficient and stable rapid imaging, improve image quality, shorten scanning time, and increase patient tolerance, which has clinical application value.
[Keywords] artificial intelligence；compressed sensing；parallel imaging；magnetic resonance imaging；shoulder

YANG Zecheng   ZHAN Yi   SHI Nannan   SHANG Ai   SHAN Fei   SHEN Jie*  

Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China

Corresponding author: SHEN J, E-mail: shenjie@shphc.org.cn

Conflicts of interest   None.

Received  2024-04-26
Accepted  2024-08-12
DOI: 10.12015/issn.1674-8034.2024.08.025
Cite this article as: YANG Z C, ZHAN Y, SHI N N, et al. Comparative use of artificial intelligence-assisted compressed sensing and parallel imaging for shoulder magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(8): 166-171. DOI:10.12015/issn.1674-8034.2024.08.025.

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