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• Special Focus •
Value of deep learning reconstruction in optimizing prostate MR T2-weighted imaging scanning time and imaging quality
WANG Yichen  ZHANG Xinxin  HU Mancang  WANG Sicong  LI Min  ZHAO Xinming  CHEN Yan 

Cite this article as: WANG Y C, ZHANG X X, HU M C, et al. Value of deep learning reconstruction in optimizing prostate MR T2-weighted imaging scanning time and imaging quality[J]. Chin J Magn Reson Imaging, 2023, 14(5): 48-52, 59. DOI:10.12015/issn.1674-8034.2023.05.010.


[Abstract] Objective To explore the application of deep learning reconstruction (DLR) in improving prostate MRI T2 weighted imaging (T2WI) quality and shortening scanning time.Materials and Methods Patients who were suspected with a prostate lesion clinically were prospectively enrolled in this study. Conventional MRI fast-spin echo (FSE)-T2WI sequence and DLR fast FSE-T2WI were performed, and the original fast FSE-T2WI without DLR was preserved. The overall image quality, image artifacts, prostate capsule, prostate lesion detection and the lesion's Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) scoring of three T2WI (conventional T2WI, fast T2WI, and DLR fast T2WI) were assessed subjectively by two radiologists independently. The signal-to-noise ratio (SNR) of the lesion and the contrast-to-noise ratio (CNR) were measured by one radiologist. One-way ANOVA and Kruskal-Wallis test were performed on normally and non-normally distributed data, respectively, to compare and analyze the differences in subjective scores and objective indices of three T2WI. The intra-class correlation coefficient (ICC) was used to compare the interreader agreement of subjective scores and PI-RADS v2.1 scoring between two radiologists.Results Finally, a total of 35 patients (38 prostate lesions) were enrolled in this study. DLR fast T2WI reduced 32.1% scanning time than conventional T2WI. Two radiologists' assessment demonstrated that there were significant differences among conventional, fast and DLR FSE-T2WI in overall image quality, prostate capsule demonstration and prostate lesion detection (P＜0.05). There were significant differences in the overall image quality, prostate capsule demonstration and prostate lesion detection among the three T2WI (P＜0.05). The SNR and CNR of prostate peripheral zone, transition zone and prostate lesion of the three T2WI images were significantly different (P＜0.05). DLR fast T2WI has the best overall image quality with the least artifacts and short scan time.Conclusions DLR can significantly improve the image quality of prostate FSE-T2WI with a shorter scanning time.
[Keywords] prostate；deep learning reconstruction；magnetic resonance image；Prostate Imaging Reporting and Data System；signal-to-noise ratio；contrast-to-noise ratio

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

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: Chen Y, E-mail: doctorchenyan626@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Beijing Hope Run Special Fund of Cancer Foundation of China (No. LC2022A12).
Received  2022-10-17
Accepted  2023-05-06
DOI: 10.12015/issn.1674-8034.2023.05.010
Cite this article as: WANG Y C, ZHANG X X, HU M C, et al. Value of deep learning reconstruction in optimizing prostate MR T2-weighted imaging scanning time and imaging quality[J]. Chin J Magn Reson Imaging, 2023, 14(5): 48-52, 59. DOI:10.12015/issn.1674-8034.2023.05.010.

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