ArticleAbdominal radiology (New York)2024
Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 14 citations in OpenAlex.
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- Deep learning reconstruction for improving image quality of pediatric abdomen MRI using a 3D T1 fast spoiled gradient echo acquisition.Pediatric radiology · 2025Article
- Compressed Sensing-Accelerated Free-Breathing Liver MRI at 7 T.NMR in biomedicine · 2025Article
- Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application.Journal of the Korean Society of Radiology · 2025Review
- AI-augmented reconstruction provides improved image quality and enables shorter breath-holds in contrast-enhanced liver MRI.European radiology experimental · 2025Article
- Deep Learning-Accelerated Non-Contrast Abbreviated Liver MRI for Detecting Malignant Focal Hepatic Lesions: Dual-Center Validation.Korean journal of radiology · 2025Article
- Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction.Korean journal of radiology · 2025Article
- Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI.Insights into imaging · 2024Article
- Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Review
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Authors and funding
6 authors at 2 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) in patients at a high risk of hepatocellular carcinoma. MATERIALS AND
methodsIn this retrospective analysis, 83 high-risk patients with hepatocellular carcinoma underwent gadoxetic acid-enhanced liver MRI using a 3-T scanner. Triple arterial phase, high-resolution portal venous phase, and high-resolution hepatobiliary phase images were reconstructed using conventional reconstruction techniques and DLRA (AIR
resultsDLRA-reconstructed images exhibited significantly improved overall image quality, image contrast, lesion conspicuity, vessel conspicuity, and liver edge sharpness and reduced subjective image noise, ringing artifacts, and motion artifacts compared to conventionally reconstructed images (all P < 0.05). Although there was no significant difference in the FOMs of non-cystic focal liver lesions between the conventional and DL methods, DLRA-reconstructed images showed notably higher pooled sensitivity than conventionally reconstructed images (P < 0.05) in all phases and higher detection rates for viable post-treatment HCCs in the arterial and hepatobiliary phases (all P < 0.05).
conclusionsImplementing DLRA can enhance the image quality in 3D T1-weighted gradient-echo sequences of gadoxetic acid-enhanced liver MRI examinations, leading to improved detection of viable post-treatment HCCs.
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