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Abstract #2741

Feasibility of High-Resolution SSFSE MR imaging using Deep Learning Reconstruction in Assessment of Ovarian Volume and Follicle Count

Renjie Yang1, Yujie Zou2, Weiyin Vivian Liu3, Zhi Wen1, Liang Li1, and Yunfei Zha1
1Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China, 2Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China, 3MR Research China, GE Healthcare, Beijing, China

Synopsis

Keywords: Image Reconstruction, Artifacts, Single-shot fast spin-echo; PROPELLER; Follicle number per ovary; Ovarian volume

Motivation: Transvaginal ultrasonography (TVUS) often underestimates follicle count (FC) compared to MRI. The repeatability of FC and ovarian volume (OV) assessment was still affected by motion artifact on conventional T2-weighted fast spin echo images.

Goal(s): To propose a more reliable MRI technique in assessing the FC and OV.

Approach: High-resolution single-shot fast spin echo (SSFSE) sequence was used to accelerate the acquisition speed, and AIRTM Recon DL was used to compensate for noise in this study.

Results: Contributing to the improved time resolution and reduced noise, SSFSE-DL demonstrated better repeatability in FC and OV assessment compared to the widely used motion-robust PROPELLER technique.

Impact: High-resolution SSFSE sequence with DL reconstruction can be a reliable imaging method in the assessment of ovarian morphology. It has a potential in determining the threshold value of FC for PCOM identification in future studies.

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Keywords