@article{QIMS154753,
author = {Elaine Yuen Phin Lee and Chia-Wei Li and Grace Ho and Chien-Yuan Lin and Andy Cheuk Nam Hwang and Rahul Singh and Patricia Lan and Xinzeng Wang},
title = {Impact of deep-learning image reconstruction on multiplexed sensitivity encoding diffusion-weighted imaging in the female pelvis},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {16},
number = {7},
year = {2026},
keywords = {},
abstract = {Background: Diffusion-weighted imaging (DWI) in the female pelvis is degraded by artefacts. The study aimed to evaluate the impact of deep-learning image reconstruction (DLRecon) on the image quality of multiplexed sensitivity encoding (MUSE) DWI in the female pelvis.Methods: Female patients scheduled for pelvic magnetic resonance imaging (MRI) with 2-shot MUSE DWI were prospectively recruited. A subset of patients underwent paired 4-shot MUSE DWI. Images were qualitatively reviewed by two radiologists in terms of overall image quality, artefacts, lesions conspicuity and sharpness using a 5-point scale, with and without DLRecon. Intra-observer and inter-observer agreements were evaluated. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of normal structures and lesions were quantitatively compared between 2-shot MUSE with and without DLRecon. Statistical analyses were performed using Cohen’s kappa, Wilcoxon signed-rank test and paired-sample t-test with Bonferroni correction.Results: Among the 65 female patients evaluated, 27 malignant lesions and 32 benign lesions were detected, while no pathology was found in 6 patients. DLRecon significantly improved the image quality in all aspects evaluated compared to 2-shot MUSE without DLRecon: overall image quality, artefacts, lesions conspicuity and sharpness (N=65, P},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/154753}
}