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Feasibility study of deep learning reconstruction in the clinical application of MRI in bladder cancer
ZHANG Xinxin  WANG Yichen  WANG Sicong  LI Min  HU Mancang  CHEN Yan  ZHAO Xinming 

Cite this article as: ZHANG X X, WANG Y C, WANG S C, et al. Feasibility study of deep learning reconstruction in the clinical application of MRI in bladder cancer[J]. Chin J Magn Reson Imaging, 2023, 14(5): 36-40. DOI:10.12015/issn.1674-8034.2023.05.008.


[Abstract] Objective To explore the impact of deep learning reconstruction (DLR) techniques on bladder cancer MRI image quality and scan time.Materials and Methods Patients with a pathological diagnosis of bladder cancer at first diagnosis were prospectively enrolled in this study. Conventional fast spin echo (FSE) T2WI, and DLR fast FSE-T2WI scanning were performed, respectively. The original fast FSE-T2WI without DLR was saved. The overall image quality score and artifacts score of three T2WI (conventional FSE-T2WI, fast FSE-T2WI, and DLR fast FSE-T2WI) were evaluated subjectively (5-point scale) by two radiologists. One radiologist measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). One-way ANOVA and Friedman test were performed on normally and non-normally distributed data, respectively, to compare and analyze the differences in SNR, CNR, overall image quality score, and artifacts score of three T2WI. The Weighted-Kappa test was used to validate the consistency of subjective scores between groups.Results A total of 32 patients [39-93 (65±11) years] with bladder cancer were enrolled in this study. The overall image quality score and artifacts score, SNR (63.2±25.5 vs. 94.7±40.8, P＜0.05) and CNR (40.0±19.0 vs. 59.6±29.8, P＜0.05) of fast FSE-T2WI with shortened scanning time were significantly lower than those of conventional FSE-T2WI. The application of DLR significantly improved the overall image quality, artifact score, SNR (256.7±102.9 vs. 63.2±25.5, P＜0.05) and CNR (168.0±77.3 vs. 40.0±19.0, P＜0.05) of fast FSE-T2WI. DLR fast FSE-T2WI demonstrated significantly higher SNR (256.7±102.9 vs. 94.7±40.8, P＜0.05) and CNR (168.0±77.3 vs. 59.6±29.8, P＜0.05) and overall image quality scores than those of conventional FSE-T2WI.Conclusions DLR allowed reduction of image scanning time and enabled improved image quality in both quantitative and qualitative manners, making it possible for bladder cancer patients to complete MRI more quickly.
[Keywords] bladder cancer；deep learning reconstruction；magnetic resonance imaging；signal-to-noise ratio；contrast-to-noise ratio

ZHANG Xinxin1   WANG Yichen1   WANG Sicong2   LI Min2   HU Mancang1   CHEN Yan1   ZHAO Xinming1*  

1 Department of Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

2 GE Healthcare, Beijing 100176, China

Corresponding author: Zhao XM, E-mail: zhaoxinming@cicams.ac.cn

Conflicts of interest   None.

Received  2022-10-08
Accepted  2023-04-28
DOI: 10.12015/issn.1674-8034.2023.05.008
Cite this article as: ZHANG X X, WANG Y C, WANG S C, et al. Feasibility study of deep learning reconstruction in the clinical application of MRI in bladder cancer[J]. Chin J Magn Reson Imaging, 2023, 14(5): 36-40. DOI:10.12015/issn.1674-8034.2023.05.008.

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