Insights into imaging · 2025
Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration.
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Zhao Dan, Kong Xiangchuang, Yang Kun, Wan Jiayu, Liu Ziyi, Pan Feng, Sun Peng, Zheng Chuansheng, Yang Lian
Аннотация
OBJECTIVES: To investigate the impact of deep learning reconstruction (DLR) on the image quality of diffusion-weighted imaging (DWI) for liver and its ability to differentiate benign from malignant focal liver lesions (FLLs).
MATERIALS AND METHODS: Consecutive patients with suspected liver disease who underwent liver MRI between January and May 2025 were included. All patients received conventional DWI (DWIC) and an accelerated reconstructed DWI (DWIDLR) in which acquisition time was prospectively halved by reducing signal averages. Image quality was compared qualitatively using Likert scores (e.g., lesion conspicuity, overall quality) and quantitatively by measuring signal-to-noise ratio of the liver (SNRLiver) and lesion (SNRLesion), contrast-to-noise ratio (CNR), and edge rise distance (ERD). Apparent diffusion coefficient (ADC) values and diagnostic performance for differentiating benign from malignant FLLs were assessed.
RESULTS: A total of 193 patients (128 males, 65 females; age range, 23-81 years) were included. For quantitative assessment, DWIDLR demonstrated higher SNRLiver, SNRLesion, CNR, and a shorter ERD (all p < 0.05). For qualitative assessment, DWIDLR showed improved lesion conspicuity, liver edge sharpness, and overall image quality (all p < 0.01), with no significant difference in artifacts (p = 0.08). ADC values were lower with DWIDLR for both benign and malignant FLLs (p < 0.001). In differentiating benign from malignant lesions, DWIDLR achieved better diagnostic performance (AUC: 0.921 vs. 0.904, p < 0.05).
CONCLUSION: Deep learning-enhanced DWI enables a 50% reduction in acquisition time while simultaneously improving liver MRI image quality and diagnostic performance in differentiating benign from malignant FLLs.
CRITICAL RELEVANCE STATEMENT: This study demonstrates that deep learning-based reconstruction enables faster, higher-quality liver MRI with improved diagnostic accuracy for focal liver lesions, supporting its integration into routine radiological practice.
KEY POINTS: Diffusion-weighted liver MRI commonly suffers from limited image quality and efficiency. Deep learning reconstruction substantially improves liver MRI quality while enabling significantly shorter acquisition times. Improved lesion differentiation enables more accurate clinical diagnosis of liver lesions.
MATERIALS AND METHODS: Consecutive patients with suspected liver disease who underwent liver MRI between January and May 2025 were included. All patients received conventional DWI (DWIC) and an accelerated reconstructed DWI (DWIDLR) in which acquisition time was prospectively halved by reducing signal averages. Image quality was compared qualitatively using Likert scores (e.g., lesion conspicuity, overall quality) and quantitatively by measuring signal-to-noise ratio of the liver (SNRLiver) and lesion (SNRLesion), contrast-to-noise ratio (CNR), and edge rise distance (ERD). Apparent diffusion coefficient (ADC) values and diagnostic performance for differentiating benign from malignant FLLs were assessed.
RESULTS: A total of 193 patients (128 males, 65 females; age range, 23-81 years) were included. For quantitative assessment, DWIDLR demonstrated higher SNRLiver, SNRLesion, CNR, and a shorter ERD (all p < 0.05). For qualitative assessment, DWIDLR showed improved lesion conspicuity, liver edge sharpness, and overall image quality (all p < 0.01), with no significant difference in artifacts (p = 0.08). ADC values were lower with DWIDLR for both benign and malignant FLLs (p < 0.001). In differentiating benign from malignant lesions, DWIDLR achieved better diagnostic performance (AUC: 0.921 vs. 0.904, p < 0.05).
CONCLUSION: Deep learning-enhanced DWI enables a 50% reduction in acquisition time while simultaneously improving liver MRI image quality and diagnostic performance in differentiating benign from malignant FLLs.
CRITICAL RELEVANCE STATEMENT: This study demonstrates that deep learning-based reconstruction enables faster, higher-quality liver MRI with improved diagnostic accuracy for focal liver lesions, supporting its integration into routine radiological practice.
KEY POINTS: Diffusion-weighted liver MRI commonly suffers from limited image quality and efficiency. Deep learning reconstruction substantially improves liver MRI quality while enabling significantly shorter acquisition times. Improved lesion differentiation enables more accurate clinical diagnosis of liver lesions.