ArticleInsights into imaging2025
Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration.
Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Intravoxel incoherent motion imaging with improved image quality by deep learning reconstruction for prediction of lymph-vascular space invasion in patients with cervical cancer.Abdominal radiology (New York) · 2026Article
- Adaptive Complex Signal Average Diffusion-Weighted MR Imaging of the Liver: Utility in Breath-Hold Imaging: A Retrospective Single-Center Study.Tomography (Ann Arbor, Mich.) · 2026Article
- A multivariate IVIM-DWI-based model for preoperative prediction of perineural invasion status in rectal cancer: a feasibility study.Magma (New York, N.Y.) · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectivesTo 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
methodsConsecutive patients with suspected liver disease who underwent liver MRI between January and May 2025 were included. All patients received conventional DWI (DWI
resultsA total of 193 patients (128 males, 65 females; age range, 23-81 years) were included. For quantitative assessment, DWI
conclusionDeep 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.
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Registered trials
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