Seung Ha Cha, Yeo Eun Han, Na Yeon Han, Min Ju Kim, Beom Jin Park, Ki Choon Sim, Deuk Jae Sung, Seulki Yoo, Patricia Lan, Arnaud Guidon
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引用次数: 0
Abstract
Background/Objectives: This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. Methods: This retrospective study included 57 patients with a visible bladder mass. DWI images were reconstructed using a vendor-provided DL algorithm (AIRTM Recon DL; GE Healthcare)-a CNN-based algorithm that reduces noise and enhances image quality-applied here as a prototype for MUSE-DWI. Two radiologists independently assessed qualitative features using a 4-point scale. For the quantitative analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) of the bladder lesions were recorded by two radiologists. The weighted kappa test and intraclass correlation were used to evaluate the interobserver agreement in the qualitative and quantitative analyses, respectively. Wilcoxon signed-rank test was used to compare the image quality of the two sequences. Results: DL MUSE-DWI demonstrated significantly improved qualitative image quality, with superior sharpness and lesion conspicuity. There were no significant differences in the distortion or artifacts. The qualitative analysis of the images by the two radiologists was in good to excellent agreement (κ ≥ 0.61). Quantitative analysis revealed higher SNR, CNR, and SIR in DL MUSE-DWI than in MUSE-DWI. The ADC values were significantly higher in DL MUSE-DWI. Interobserver agreement was poor (ICC ≤ 0.32) for SNR and CNR and excellent (ICC ≥ 0.85) for SIR and ADC values in both DL MUSE-DWI and MUSE-DWI. Conclusions: DL MUSE-DWI significantly enhanced the image quality in terms of lesion sharpness, conspicuity, SNR, CNR, and SIR, making it a promising tool for clinical imaging.
DiagnosticsBiochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍:
Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.