Deep Learning Reconstruction EOB-MRI detects more small low-signal lesions than standard non-DL MRI in 53 patients

ObjectiveIn recent years, the application of Deep Learning Reconstruction (DL Recon) technology in MRI has significantly improved the detection rate of small focal liver lesions (FLLs). This study aims to compare the detection performance of DL Recon EOB-MRI with traditional Gd-EOB-DTPA-enhanced liver MRI during the hepatobiliary phase (HBP) for FLL detection.MethodsThis prospective, single-center study included 53 patients who underwent EOB-MRI during the hepatobiliary phase (HBP). For each patient, four types of images were acquired: 3 mm thickness non-DL reconstruction (Standard Non-DL), 3 mm thickness DL reconstruction (Standard DL), 1 mm thickness non-DL reconstruction (HR Non-DL), and 1 mm thickness DL reconstruction (HR-DL). Lesions were categorized based on signal intensity into high-signal and low-signal types, and classified into five size groups: >30 mm, 20–30 mm, 10–20mm, 5–10mm, and 0.90, indicating excellent consistency (P < 0.001). For high-signal lesions, no differences were observed across groups (except for the total number of high-signal lesions: Standard DL vs. Standard Non-DL: P†† = 0.048, all other P > 0.05). For large low-signal lesions (>10 mm), no significant differences were found between groups (P > 0.05). For small low-signal lesions (5–10 mm), the Standard DL group showed significantly more lesions than the Standard Non-DL group (P†† = 0.030). For small low-signal lesions (