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

Radiology Artificial Intelligence 2026 Open access

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Authors: Yunfei Zhang, Zhijun Geng, Xianling Qian, Yuyao Xiao, Ruofan Sheng, Mengsu Zeng
DOI: 10.1148/ryai.250914
Cited by: 1
References: 33

Abstract

A deep learning-based single breath-hold abbreviated MRI protocol for hepatocellular carcinoma diagnosis was developed, enabling gadolinium-free, ultrafast imaging while preserving full-sequence diagnostic information and demonstrating performance comparable to that of conventional complete MRI.

Fields of Study & Subjects

Hepatocellular Carcinoma Treatment and Prognosis MRI in cancer diagnosis Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Radiology Artificial Intelligence
Publisher
Radiological Society of North America (RSNA)
Volume
8
Issue
4
Pages
e250914–e250914
Publication date
20 May 2026
Language
EN