Share this content in WeChat
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:

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.

GIROMETTI R, CERESER L, BONATO F, et al. Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies[J/OL]. Eur Radiol Exp, 2019, 3(1): 5 [2022-11-01]. DOI: 10.1186/s41747-019-0088-3">10.1186/s41747-019-0088-3">10.1186/s41747-019-0088-3.
BONEKAMP D, JACOBS M A, EL-KHOULI R, et al. Advancements in MR imaging of the prostate: from diagnosis to interventions[J]. Radiographics, 2011, 31(3): 677-703. DOI: 10.1148/rg.313105139">10.1148/rg.313105139">10.1148/rg.313105139.
WOO S, GHAFOOR S, VARGAS H A. Contribution of radiology to staging of prostate cancer[J]. Semin Nucl Med, 2019, 49(4): 294-301. DOI: 10.1053/j.semnuclmed.2019.02.007">10.1053/j.semnuclmed.2019.02.007">10.1053/j.semnuclmed.2019.02.007.
SCHAEFFER E, SRINIVAS S, ANTONARAKIS E S, et al. NCCN guidelines insights: prostate cancer, version 1.2021[J]. J Natl Compr Canc Netw, 2021, 19(2): 134-143. DOI: 10.6004/jnccn.2021.0008">10.6004/jnccn.2021.0008">10.6004/jnccn.2021.0008.
HE J, CHEN W Q, LI N, et al. China guideline for the screening and early detection of prostate cancer (2022, Beijing)[J]. Chin J Oncol, 2022, 44(1): 29-53. DOI: 10.3760/cma.j.cn112152-20211226-00975">10.3760/cma.j.cn112152-20211226-00975">10.3760/cma.j.cn112152-20211226-00975.
Chinese Prostate Cancer Consortium of Chinese, Urological Association of Chinese Medical Association. China expert consensus on prostate puncture[J]. Chin J Urol, 2016, 37(4): 241-245. DOI: 10.3760/cma.j.issn.1000-6702.2016.04.001">10.3760/cma.j.issn.1000-6702.2016.04.001">10.3760/cma.j.issn.1000-6702.2016.04.001.
CORNFORD P, BELLMUNT J, BOLLA M, et al. EAU-ESTRO-SIOG guidelines on prostate cancer. part Ⅱ: treatment of relapsing, metastatic, and castration-resistant prostate cancer[J]. Eur Urol, 2017, 71(4): 630-642. DOI: 10.1016/j.eururo.2016.08.002">10.1016/j.eururo.2016.08.002">10.1016/j.eururo.2016.08.002.
TURKBEY B, ROSENKRANTZ A B, HAIDER M A, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2[J]. Eur Urol, 2019, 76(3): 340-351. DOI: 10.1016/j.eururo.2019.02.033">10.1016/j.eururo.2019.02.033">10.1016/j.eururo.2019.02.033.
SACKETT J, SHIH J H, REESE S E, et al. Quality of prostate MRI: is the PI-RADS standard sufficient?[J]. Acad Radiol, 2021, 28(2): 199-207. DOI: 10.1016/j.acra.2020.01.031">10.1016/j.acra.2020.01.031">10.1016/j.acra.2020.01.031.
LOENING A M, LITWILLER D V, SARANATHAN M, et al. Increased speed and image quality for pelvic single-shot fast spin-echo imaging with variable refocusing flip angles and full-Fourier acquisition[J]. Radiology, 2017, 282(2): 561-568. DOI: 10.1148/radiol.2016151574">10.1148/radiol.2016151574">10.1148/radiol.2016151574.
UEDA T, OHNO Y, YAMAMOTO K, et al. Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice[J/OL]. Eur J Radiol, 2021, 134: 109430 [2022-11-01]. DOI: 10.1016/j.ejrad.2020.109430">10.1016/j.ejrad.2020.109430">10.1016/j.ejrad.2020.109430.
LEE D, YOO J, TAK S, et al. Deep residual learning for accelerated MRI using magnitude and phase networks[J]. IEEE Trans Biomed Eng, 2018, 65(9): 1985-1995. DOI: 10.1109/TBME.2018.2821699">10.1109/TBME.2018.2821699">10.1109/TBME.2018.2821699.
HAMMERNIK K, KLATZER T, KOBLER E, et al. Learning a variational network for reconstruction of accelerated MRI data[J]. Magn Reson Med, 2018, 79(6): 3055-3071. DOI: 10.1002/mrm.26977">10.1002/mrm.26977">10.1002/mrm.26977.
AGGARWAL H K, MANI M P, JACOB M. MoDL: model-based deep learning architecture for inverse problems[J]. IEEE Trans Med Imaging, 2019, 38(2): 394-405. DOI: 10.1109/TMI.2018.2865356">10.1109/TMI.2018.2865356">10.1109/TMI.2018.2865356.
UEDA T, OHNO Y, YAMAMOTO K, et al. Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging[J]. Radiology, 2022, 303(2): 373-381. DOI: 10.1148/radiol.204097">10.1148/radiol.204097">10.1148/radiol.204097.
GASSENMAIER S, AFAT S, NICKEL M D, et al. Accelerated T2-weighted TSE imaging of the prostate using deep learning image reconstruction: a prospective comparison with standard T2-weighted TSE imaging[J/OL]. Cancers, 2021, 13(14): 3593 [2022-11-01]. DOI: 10.3390/cancers13143593">10.3390/cancers13143593">10.3390/cancers13143593.
GASSENMAIER S, AFAT S, NICKEL D, et al. Deep learning-accelerated T2-weighted imaging of the prostate: reduction of acquisition time and improvement of image quality[J/OL]. Eur J Radiol, 2021, 137: 109600 [2022-11-01]. DOI: 10.1016/j.ejrad.2021.109600.
TONG A, BAGGA B, PETROCELLI R, et al. Comparison of a deep learning-accelerated vs. conventional T2-weighted sequence in biparametric MRI of the prostate[J/OL]. J Magn Reson Imaging, 2023 [2023-04-28]. DOI: 10.1002/jmri.28602">10.1002/jmri.28602">10.1002/jmri.28602.
KIM E H, CHOI M H, LEE Y J, et al. Deep learning-accelerated T2-weighted imaging of the prostate: impact of further acceleration with lower spatial resolution on image quality[J/OL]. Eur J Radiol, 2021, 145: 110012 [2022-11-01]. DOI: 10.1016/j.ejrad.2021.110012">10.1016/j.ejrad.2021.110012">10.1016/j.ejrad.2021.110012.
PARK J C, PARK K J, PARK M Y, et al. Fast T2-weighted imaging with deep learning-based reconstruction: evaluation of image quality and diagnostic performance in patients undergoing radical prostatectomy[J]. J Magn Reson Imaging, 2022, 55(6): 1735-1744. DOI: 10.1002/jmri.27992">10.1002/jmri.27992">10.1002/jmri.27992.
HIGAKI T, NAKAMURA Y, TATSUGAMI F, et al. Improvement of image quality at CT and MRI using deep learning[J]. Jpn J Radiol, 2019, 37(1): 73-80. DOI: 10.1007/s11604-018-0796-2">10.1007/s11604-018-0796-2">10.1007/s11604-018-0796-2.
QIU D F, ZHANG S X, LIU Y, et al. Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning[J/OL]. Comput Methods Programs Biomed, 2020, 187: 105059 [2022-11-01]. DOI: 10.1016/j.cmpb.2019.105059">10.1016/j.cmpb.2019.105059">10.1016/j.cmpb.2019.105059.
KIDOH M, SHINODA K, KITAJIMA M, et al. Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers[J]. Magn Reson Med Sci, 2020, 19(3): 195-206. DOI: 10.2463/">10.2463/">10.2463/
GIROMETTI R, GIANNARINI G, GRECO F, et al. Interreader agreement of PI-RADS v. 2 in assessing prostate cancer with multiparametric MRI: a study using whole-mount histology as the standard of reference[J]. J Magn Reson Imaging, 2019, 49(2): 546-555. DOI: 10.1002/jmri.26220">10.1002/jmri.26220">10.1002/jmri.26220.
BARRETT T, GHAFOOR S, GUPTA R T, et al. Prostate MRI qualification: AJR expert panel narrative review[J]. Am J Roentgenol, 2022, 219(5): 691-702. DOI: 10.2214/ajr.22.27615">10.2214/ajr.22.27615">10.2214/ajr.22.27615.

PREV Novel deep learning-based T2-weighted imaging of the prostate provides superior image quality
NEXT Clinical feasibility of 2D FSE sequences of the knee MRI protocol using deep-learning image reconstruction

Tel & Fax: +8610-67113815    E-mail: