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• Technical Article •
Application of artificial intelligence-assisted compressed sensing technology in brain 3D T2-FLAIR sequence acquisition and evaluation of white matter hyperintensity
CAO Jiajun  LIU Na  ZHONG Meimeng  YUAN Chang  ZHANG Yukun  MIAO Yanwei  SONG Qingwei 

Cite this article as: CAO J J, LIU N, ZHONG M M, et al. Application of artificial intelligence-assisted compressed sensing technology in brain 3D T2-FLAIR sequence acquisition and evaluation of white matter hyperintensity[J]. Chin J Magn Reson Imaging, 2024, 15(2): 135-139, 146. DOI:10.12015/issn.1674-8034.2024.02.020.


[Abstract] Objective To investigate the effects of different acceleration factors based on artificial intelligence-assisted compressed sensing (ACS) on the image quality of 3D T2WI fluid-attenuated inversion-recovery (3D T2-FLAIR) sequence.Materials and Methods Twenty-five healthy volunteers (HC) and fifteen patients with white matter hyperintensity (WMH) were prospectively included in the study. In HC group, the brain 3D T2-FLAIR images were collected by parallel imaging (PI) technique (parallel acquisition acceleration factor was 3) and ACS technique with different acceleration factors (3, 4, 5, 6, 7, 8). The signal intensity (SI) and standard deviation (SD) of all 3D T2-FLAIR images were measured in bilateral centrum semiovale, bilateral caudate nucleus, splenium of corpus callosum, bilateral red nucleus, bilateral substantia nigra, pons and bilateral cerebellum. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were further calculated. The subjective score of image quality was analyzed according to five grades standard. The intra-class correlation coefficient (ICC) and Kappa test were used to compare the consistency between the measured data and the subjective scores of the two observers. The SNR, CNR and subjective scores of images with different acceleration factors were compared by Friedman test. After comprehensive evaluation, the best ACS acceleration factor is obtained. In the WMH group, 3D T2-FLAIR images of the brain were collected with F3 and the best ACS acceleration factor, and the number of WMH and Fazekas grades were evaluated by two experienced diagnostic physicians. Independent sample t test and Mann-Whitney U test were used for comparative analysis.Results In HC group, The SNR, CNR and subjective scores of different 3D T2-FLAIR sequences were statistically significant (all P<0.05). The results of pairwise comparison showed that the SNR and CNR of 3D T2-FLAIRACS3, 3D T2-FLAIRACS4 and 3D T2-FLAIRF3, and the subjective scores of 3D T2-FLAIRACS3, 3D T2-FLAIRACS4, 3D T2-FLAIRACS5 and 3D T2-FLAIRF3 were not statistically significant (all P>0.05). The SNR, CNR and subjective scores of the remaining images were statistically significant (all P<0.05). In the WMH group, there was no significant difference in the number of WMH and Fazekas grades between 3D T2-FLAIR F3 and 3D T2-FLAIR ACS4 ( P>0.05 ).Conclusions The acquisition of brain 3D T2-FLAIR with ACS technology can shorten the scanning time under the premise of ensuring image quality and diagnostic efficiency, and ACS4 can be considered as the best acceleration factor.
[Keywords] artificial intelligence compressed sensing；compressed sensing；magnetic resonance imaging；brain；acceleration

CAO Jiajun   LIU Na   ZHONG Meimeng   YUAN Chang   ZHANG Yukun   MIAO Yanwei   SONG Qingwei*  

Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

Corresponding author: SONG Q W, E-mail: songqw1964@163.com

Conflicts of interest   None.

Received  2023-08-10
Accepted  2024-01-31
DOI: 10.12015/issn.1674-8034.2024.02.020
Cite this article as: CAO J J, LIU N, ZHONG M M, et al. Application of artificial intelligence-assisted compressed sensing technology in brain 3D T2-FLAIR sequence acquisition and evaluation of white matter hyperintensity[J]. Chin J Magn Reson Imaging, 2024, 15(2): 135-139, 146. DOI:10.12015/issn.1674-8034.2024.02.020.

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