AI Article Synopsis

  • The study aimed to evaluate the effectiveness of high-resolution single-shot fast spin-echo (SSFSE) imaging enhanced with a deep learning reconstruction algorithm for counting follicles in ovaries of patients with polycystic ovary syndrome (PCOS).
  • Results showed that SSFSE images with deep learning (SSFSE-DL) had fewer blurring artifacts and better clarity compared to original SSFSE and conventional fast spin-echo (FSE) images.
  • The SSFSE-DL images also demonstrated superior repeatability and agreement among observers when counting follicles, indicating better diagnostic performance than the other methods.

Article 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 ( < 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 ( < 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179356PMC
http://dx.doi.org/10.3390/jcm12093234DOI Listing

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