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Technical Article
Study on acceleration efficiency and image quality of artificial intelligence compressed sensing and compressed sensing in knee MRI
PAN Ke  LIU Qianqian  TANG Lingling  HU Yuntao  HUANG Xiaohua 

Cite this article as: Pan K, Liu QQ, Tang LL, et al. Study on acceleration efficiency and image quality of artificial intelligence compressed sensing and compressed sensing in knee MRI[J]. Chin J Magn Reson Imaging, 2022, 13(5): 94-98. DOI:10.12015/issn.1674-8034.2022.05.017.

[Abstract] Objective To evaluate the utility of compressed sensing (CS) and artificial intelligence compressed sensing (ACS) in knee magnetic resonance imaging (MRI) and the effect on image quality.Materials and Methods Sixty-seven subjects were scanned with a 3.0 T scanner, undergoing proton density weighted imaging (PDWI) using three CS factors (CS 2.0, CS 2.5, CS 3.0), four ACS factors (ACS 2.5, ACS 3.0, ACS 3.5, ACS 4.0), with parallel imaging (PI) 2.0 as the reference. Two independent readers rated the overall image quality with a 4-point subjective scale. ROIs were placed on media femoral condyle, the medial head of gastrocnemius muscle, the intercondylar fossa effusion, the infrapatellar fat pad and lateral condylar cartilage of femur to measure signal intensity and noise, then signal-to-noise (SNR) were calculated as objective index. For the subjective scoring inter-rater reliability of the two readers was calculated using Cohen kappa statistics and the subjective scores and objective index were evaluated using Kruskal-Walli's test and Wilcoxon signed rank test with SPSS 23.0 software.Results Scan time decreased with increasing acceleration factors (PI 2.0: 152 s; CS 2.0: 128 s, CS 2.5: 104 s, CS 3.0: 86 s; ACS 2.5: 76 s, ACS 3.0: 65 s, ACS 3.0: 57 s, ACS 4.0: 51 s). The results of subjective scores by two readers showed strong agreement and almost perfect agreement (0.735≤κ≤0.869). There was significant difference in subjective scores and SNR among the 8 groups (P<0.05). There was no significant difference in subjective scores and 5 SNRs between CS 2.0, ACS 3.0 and PI 2.0 respectively (P>0.05). There was no significant difference in subjective scores and 4 SNRs between ACS 2.5 and PI 2.0, while a SNR of ACS 2.5 was higher than that of PI 2.0 significantly (P<0.05). The subjective scores of CS 2.5, CS 3.0, ACS 3.5, ACS 4.0 were lower than that of PI 2.0 significantly, and there was 1, 5, 2, 2 SNRs lower than those of PI 2.0 respectively(P<0.05).Conclusions ACS and CS could accelerate shorten MR acquisition time. Compared with CS, ACS was more effectively, and ACS could reduce the acquisition time by 57% on the premise of ensuring image quality in keen PDWI.
[Keywords] artificial intelligence compressed sensing;compressed sensing;magnetic resonance imaging;knee;acceleration

PAN Ke   LIU Qianqian   TANG Lingling   HU Yuntao   HUANG Xiaohua*  

Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China

Huang XH, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS 2020 Nanchong Applied Technology Research and Development Project (No. 20YFZJ0108).
Received  2021-10-22
Accepted  2022-04-28
DOI: 10.12015/issn.1674-8034.2022.05.017
Cite this article as: Pan K, Liu QQ, Tang LL, et al. Study on acceleration efficiency and image quality of artificial intelligence compressed sensing and compressed sensing in knee MRI[J]. Chin J Magn Reson Imaging, 2022, 13(5): 94-98. DOI:10.12015/issn.1674-8034.2022.05.017.

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