Development and Validation of a Deep Learning-enabled Single Breath-hold Abbreviated MRI Protocol for Hepatocellular...

Purpose To develop a deep learning-enabled single breath-hold abbreviated MRI (DL-SBH-aMRI) protocol for hepatocellular carcinoma (HCC) diagnosis. Materials and Methods Patients at high risk for HCC from four institutions (January 2019-January 2025) were prospectively and retrospectively included. All patients underwent conventional complete MRI (cMRI) examinations, including precontrast T1-weighted imaging (pre-T1); postcontrast T1-weighted imaging in arterial, portal venous, and delayed phases; T2-weighted imaging (T2WI); diffusion-weighted imaging (DWI); and apparent diffusion coefficient mapping. Four generative models were trained to synthesize full MRI sequences (T2WI, DWI, apparent diffusion coefficient, arterial phase, portal venous phase, delayed phase) from pre-T1. The best-performing model was selected to generate synthetic sequences, which, combined with pre-T1 acquired from MRI, formed the DL-SBH-aMRI protocol. Image quality, perceptual realism, and lesion size measurement accuracy were evaluated for DL-SBH-aMRI versus cMRI; diagnostic performance at the patient and lesion levels was assessed using a reference standard based on histopathology and imaging findings. Results A total of 1008 patients were included (mean age ± SD, 56.8 years ± 11.8; 700 male patients). The diffusion-based generative model (Li-DiffNet) yielded the highest synthetic image quality and was selected as the backbone of DL-SBH-aMRI. DL-SBH-aMRI was noninferior to cMRI in subjective image quality scores (4.07-4.16 vs 4.18-4.19; P < .001). DL-SBH-aMRI demonstrated noninferior diagnostic performance (all P < .001) for HCC at the patient and lesion levels (sensitivity of 77.9%-88.7%; specificity of 91.6%-93.1%) compared with cMRI (sensitivity of 84.4%-92.5%; specificity of 94.1%-95.2%). Conclusion The DL-SBH-aMRI protocol may enable gadolinium-free, rapid MRI of the liver while preserving full-sequence diagnostic information for HCC diagnosis. Keywords: Hepatocellular Carcinoma, Deep Learning, Abbreviated MRI, Diagnostic Evaluation, Image Generation Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. See also commentary by Wang and Gu in this issue.