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Application value of high-resolution single-shot fast spin-echo ovarian MRI based on deep learning reconstruction in follicle counting
YANG Renjie  ZOU Yujie  LIU Weiyin  LIU Changsheng  WEN Zhi  LI Liang  ZHA Yunfei 

Cite this article as: YANG R J, ZOU Y J, LIU W Y, et al. Application value of high-resolution single-shot fast spin-echo ovarian MRI based on deep learning reconstruction in follicle counting[J]. Chin J Magn Reson Imaging, 2024, 15(10): 50-55, 61. DOI:10.12015/issn.1674-8034.2024.10.009.


[Abstract] Objective To investigate the application value of high-resolution single-shot fast spin-echo (SSFSE) acquisition with deep learning reconstruction (DLR) in follicle counting compared to transvaginal ultrasonography (TVUS), conventional reconstruction (CR) SSFSE and periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) images.Materials and Methods Participants with clinically confirmed or suspected polycystic ovary syndrome (PCOS) were prospectively recruited and underwent ovarian MRI. Those with no history of sexual activity also underwent ovarian TVUS. High-resolution PROPELLER and SSFSE T2-weighted sequences were obtained on three matched planes. The SSFSE sequences implemented both DLR and CR, generating SSFSE-DLR and SSFSE-CR images respectively. Qualitative indicators including blurring artifacts, subjective noise, and conspicuity of follicles were compared using Wilcoxon signed-rank tests. Follicle counting was performed by two observers, with repeatability assessed using intraclass correlation coefficient (ICC) and Bland-Altman method. Absolute values of intra-observer and inter-observer differences were compared using a paired t-test. Follicle count between SSFSE-DL and TVUS was also compared using a paired t-test.Results Twenty-four participants underwent MRI, with 18 of them also undergoing TVUS. Observer 1 assigned higher subjective scores to SSFSE-DLR in comparison to SSFSE-CR and PROPELLER (P<0.05), despite the similar subjective noise observed between SSFSE-DLR and PROPELLER (P>0.05). Observer 2 also rated SSFSE-DLR higher than SSFSE-CR and PROPELLER (P<0.05). Furthermore, SSFSE-DLR demonstrated the best repeatability for follicle counting, achieving the highest ICC, narrowest 95% limits of agreement, and the lowest absolute values of intra-observer and inter-observer differences (P<0.05). Moreover, SSFSE-DL detected more follicles than TVUS (P<0.001).Conclusions SSFSE-DLR images significantly improved the display of ovarian morphology and the repeatability of follicle counting, thereby fortifying the reliability of future polycystic ovary morphology determinations.
[Keywords] polycystic ovary syndrome；polycystic ovary；follicle count；deep learning reconstruction；single-shot fast spin-echo；magnetic resonance imaging

YANG Renjie1   ZOU Yujie2   LIU Weiyin3   LIU Changsheng1   WEN Zhi1   LI Liang1   ZHA Yunfei1*  

1 Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

2 Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan 430060, China

3 GE Healthcare (China) Co., Ltd., Beijing 100176, China

Corresponding author: ZHA Y F, E-mail: zhayunfei999@126.com

Conflicts of interest   None.

Received  2024-01-18
Accepted  2024-05-31
DOI: 10.12015/issn.1674-8034.2024.10.009
Cite this article as: YANG R J, ZOU Y J, LIU W Y, et al. Application value of high-resolution single-shot fast spin-echo ovarian MRI based on deep learning reconstruction in follicle counting[J]. Chin J Magn Reson Imaging, 2024, 15(10): 50-55, 61. DOI:10.12015/issn.1674-8034.2024.10.009.

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