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Article

High-Resolution Single-Shot Fast Spin-Echo MR Imaging with Deep Learning Reconstruction Algorithm Can Improve Repeatability and Reproducibility of Follicle Counting

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
MR Research, GE Healthcare, Beijing 100080, China
4
First School of Clinical Medicine of Wuhan University, Wuhan 430060, China
5
Department of Obstetrics, Renmin Hospital of Wuhan University, Wuhan 430060, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Clin. Med. 2023, 12(9), 3234; https://doi.org/10.3390/jcm12093234
Submission received: 11 February 2023 / Revised: 30 March 2023 / Accepted: 28 April 2023 / Published: 30 April 2023
(This article belongs to the Special Issue New Advances in Clinical Reproductive Medicine)

Abstract

Objective: To investigate the diagnostic performance of high-resolution single-shot fast spin-echo (SSFSE) imaging with deep learning (DL) reconstruction algorithm on follicle counting and compare it with original SSFSE images and conventional fast spin-echo (FSE) images. Methods: This study included 20 participants (40 ovaries) with clinically confirmed polycystic ovary syndrome (PCOS) who underwent high-resolution ovary MRI, including three-plane T2-weighted FSE sequences and slice-matched T2-weighted SSFSE sequences. A DL reconstruction algorithm was applied to the SSFSE sequences to generate SSFSE-DL images, and the original SSFSE images were also saved. Subjective evaluations such as the blurring artifacts, subjective noise, and clarity of the follicles on the SSFSE-DL, SSFSE, and conventional FSE images were independently conducted by two observers. Intra-class correlation coefficients and Bland–Altman plots were used to present the repeatability and reproducibility of the follicle number per ovary (FNPO) based on the three types of images. Results: SSFSE-DL images showed less blurring artifact, subjective noise, and better clarity of the follicles than SSFSE and FSE (p < 0.05). For the repeatability of the FNPO, SSFSE-DL showed the highest intra-observer (ICC = 0.930; 95% CI: 0.878–0.962) and inter-observer (ICC = 0.914; 95% CI: 0.843–0.953) agreements. The inter-observer 95% limits of agreement (LOA) for SSFSE-DL, SSFSE, and FSE ranged from −3.7 to 4.5, −4.4 to 7.0, and −7.1 to 7.6, respectively. The intra-observer 95% LOA for SSFSE-DL, SSFSE, and FSE ranged from −3.5 to 4.0, −5.1 to 6.1, and −5.7 to 4.2, respectively. The absolute values of intra-observer and inter-observer differences for SSFSE-DL were significantly lower than those for SSFSE and FSE (p < 0.05). Conclusions: Compared with the original SSFSE images and the conventional FSE images, high-resolution SSFSE images with DL reconstruction algorithm can better display follicles, thus improving FNPO assessment.
Keywords: single-shot fast spin-echo; deep learning reconstruction; fast spin-echo; polycystic ovary syndrome; follicle count; follicle number per ovary single-shot fast spin-echo; deep learning reconstruction; fast spin-echo; polycystic ovary syndrome; follicle count; follicle number per ovary

Share and Cite

MDPI and ACS Style

Yang, R.; Zou, Y.; Liu, W.; Liu, C.; Wen, Z.; Li, L.; Sun, C.; Hu, M.; Zha, Y. High-Resolution Single-Shot Fast Spin-Echo MR Imaging with Deep Learning Reconstruction Algorithm Can Improve Repeatability and Reproducibility of Follicle Counting. J. Clin. Med. 2023, 12, 3234. https://doi.org/10.3390/jcm12093234

AMA Style

Yang R, Zou Y, Liu W, Liu C, Wen Z, Li L, Sun C, Hu M, Zha Y. High-Resolution Single-Shot Fast Spin-Echo MR Imaging with Deep Learning Reconstruction Algorithm Can Improve Repeatability and Reproducibility of Follicle Counting. Journal of Clinical Medicine. 2023; 12(9):3234. https://doi.org/10.3390/jcm12093234

Chicago/Turabian Style

Yang, Renjie, Yujie Zou, Weiyin (Vivian) Liu, Changsheng Liu, Zhi Wen, Liang Li, Chenyu Sun, Min Hu, and Yunfei Zha. 2023. "High-Resolution Single-Shot Fast Spin-Echo MR Imaging with Deep Learning Reconstruction Algorithm Can Improve Repeatability and Reproducibility of Follicle Counting" Journal of Clinical Medicine 12, no. 9: 3234. https://doi.org/10.3390/jcm12093234

APA Style

Yang, R., Zou, Y., Liu, W., Liu, C., Wen, Z., Li, L., Sun, C., Hu, M., & Zha, Y. (2023). High-Resolution Single-Shot Fast Spin-Echo MR Imaging with Deep Learning Reconstruction Algorithm Can Improve Repeatability and Reproducibility of Follicle Counting. Journal of Clinical Medicine, 12(9), 3234. https://doi.org/10.3390/jcm12093234

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