ArticleEuropean radiology experimental2025
AI-augmented reconstruction provides improved image quality and enables shorter breath-holds in contrast-enhanced liver MRI.
Article in European radiology experimental, 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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3 citing papers in PubMed.
- Deep learning-based high-resolution united compressed sensing for gadoxetic acid-enhanced liver magnetic resonance imaging in the detection of colorectal liver metastases.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning reconstruction accelerated reduced field-of-view DWI in rectal cancer: mucosa-submucosa-muscularis visualization and T staging.European radiology experimental · 2026Article
- Recent advances in artificial intelligence-assisted medical imaging education.Frontiers in medicine · 2026Review
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11 authors.
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Abstract
backgroundTo compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI).
methodsFifty patients undergoing gadoxetate-enhanced MRI were recruited. Two T1-weighted Dixon sequences were utilized: a 17-s breath-hold acquisition and an accelerated 12-s breath-hold acquisition (reduced phase resolution), both reconstructed using neural network (NN) and iterative denoising (ID), NN-alone, ID-alone, and the standard method. Contrast-to-noise ratio (CNR) was assessed quantitatively for all series (ANOVA). Two blinded radiologists independently analyzed three image sets: 17-s acquisition reconstructed with NN and ID (17-s NN + ID), 12-s acquisition reconstructed with NN and ID (12-s NN + ID), and 17-s acquisition with standard reconstruction (17-s standard). Overall image quality, qualitative CNR, lesion edge sharpness, vessel edge sharpness, and respiratory motion artifacts were scored (4-point Likert scale) and compared (Friedman test). Lesion detection was compared between 12-s NN + ID and 17-s standard reconstructions (Wilcoxon signed-rank test).
resultsQuantitative liver-to-portal vein CNR was significantly higher for 17-s NN + ID than 17-s standard or 17-s NN-alone images (p = 0.001). Scores for overall image quality, qualitative CNR, vessel edge sharpness, and lesion edge sharpness were significantly higher for 17-s NN + ID and 12-s NN + ID than standard reconstruction (p < 0.001); there was no significant difference between 17-s and 12-s NN + ID. There was no significant difference in respiratory motion artifacts and number of lesions or diameter of the smallest detected lesion using 12-s NN + ID or 17-s standard reconstruction.
conclusionAI-augmented reconstructions can improve image quality while reducing breath-hold duration in T1-weighted hepatobiliary-phase gadoxetate-enhanced MRI, without compromising lesion detection. RELEVANCE STATEMENT: AI-augmented reconstruction of T1-weighted MRI improves image quality and lesion detection in hepatobiliary phase liver imaging, reducing breath-hold duration without compromising clinical lesion detection. KEY POINTS: Liver-to-portal vein CNR was significantly higher for 17-s NN + ID. AI-augmented reconstructions scored higher for image quality, contrast-to-noise, vessel-edge, and lesion-edge sharpness. No significant difference in lesion detection between 12-s NN + ID and 17-s standard reconstructions.
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