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• Technical Article •
Application value of diffusion-weighted imaging based on deep learning reconstruction algorithm in cranial MRI examination
ZHANG Yanhua  YU Renqiang  YU Bin  WU Zhiwei  ZHAO Chungang  WAN Lu  WAN Chengxin  ZHANG Zhiwei 

Cite this article as: ZHANG Y H, YU R Q, YU B, et al. Application value of diffusion-weighted imaging based on deep learning reconstruction algorithm in cranial MRI examination[J]. Chin J Magn Reson Imaging, 2025, 16(7): 65-71. DOI:10.12015/issn.1674-8034.2025.07.010.


[Abstract] Objective To explore the application value of diffusion weighted imaging (DWI) based on deep learning reconstruction algorithm (DLR) in cranial MRI examination.Materials and Methods A retrospective analysis was conducted on the MRI imaging data of 40 patients with intracranial space occupying lesions. Four sets of image quality differences were compared between conventional reconstruction (c2-DWI, c1-DWI) and DLR (DL2-DWI, DL1-DWI) with a number of excitations (NEX) of 2 and 1. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of gray and white matter were compared, as well as the apparent diffusion coefficient (ADC) of the lesion area and the contralateral normal area. Two physicians used a double-blind method to score the overall image quality, noise level, and magnetic susceptibility artifacts using a 5-point scale.Results The SNR and CNR of DLR sequence gray matter and white matter were higher than those of conventional reconstructed sequence, and the difference was statistically significant (P < 0.001). There was no statistically significant difference in ADC values between the lesion area and the contralateral normal area (P > 0.05). The overall image quality and noise level scores of DLR are higher than those of conventional reconstruction, and the difference is statistically significant (P < 0.001). There was no statistically significant difference in magnetic sensitivity artifacts (P > 0.05).Conclusions DLR can significantly improve the SNR, CNR, and subjective score of DWI images, effectively reducing image noise. While NEX is halved and scanning time is shortened, although there is limited improvement in magnetic sensitivity artifacts, it does not affect the accuracy of ADC values.
[Keywords] diffusion weighted imaging；apparent diffusion coefficient；number of excitations；deep learning reconstruction；intracranial space-occupying lesions；magnetic resonance imaging

ZHANG Yanhua1   YU Renqiang2   YU Bin2   WU Zhiwei1   ZHAO Chungang1   WAN Lu3   WAN Chengxin2   ZHANG Zhiwei2*  

1 Department of Radiology, Dazhou Central Hospital, Dazhou 635000, China

2 Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

3 Department of Radiology, Chongqing Red Cross Hospital (Jiangbei District People's Hospital), Chongqing 400020, China

Corresponding author: ZHANG Z W, E-mail: zhangzhiweicqmu@163.com

Conflicts of interest   None.

Received  2024-11-15
Accepted  2025-07-06
DOI: 10.12015/issn.1674-8034.2025.07.010
Cite this article as: ZHANG Y H, YU R Q, YU B, et al. Application value of diffusion-weighted imaging based on deep learning reconstruction algorithm in cranial MRI examination[J]. Chin J Magn Reson Imaging, 2025, 16(7): 65-71. DOI:10.12015/issn.1674-8034.2025.07.010.

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