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深度学习授权无加多增强对比缩短的MRI用于诊断肝细胞癌

Yunfei Zhang^1,2, Ruofan Sheng¹, Xianling Qian¹

¹Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

JHEP reports : innovation in hepatology

|May 8, 2025

概括

基于深度学习的缩写MRI (DL-aMRI) 提供了一种更快,没有对比剂的方法来检测肝细胞癌 (HCC). 这种先进的技术与传统的MRI精度相匹配,同时显著缩短扫描时间,改善患者护理.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

肝细胞癌 (HCC) 检测对于患者的治疗结果至关重要.
缩写型MRI (aMRI) 在减少HCC检测扫描时间方面表现有前途.
基于加多的对比剂带有潜在的风险,增加成本.

研究的目的:

开发和评估基于深度学习的,不含加多的对比增强缩短MRI (DL-aMRI) 协议,用于检测HCC.
为了比较DL-aMRI的诊断性能和效率与传统的完整MRI (cMRI).

主要方法:

一项多中心回顾性研究包括1769名患者 (913名HCC患者).
基于稳定扩散的深度学习模型生成了合成对比增强MRI (CE-MRI) 序列.
与cMRI协议相比,DL-aMRI协议结合了非对比的MRI和DL合成的CE-MRI.

主要成果:

与cMRI相比,DL-aMRI显著减少了采集时间,从28.1分钟减少到4.1分钟.
DL合成的CE-MRI的图像质量与实际的CE-MRI没有劣势 (p < 0.001).
对于DL-aMRI (0.866/0.922) 的聚合灵敏度和特异性与cMRI (0.899/0.925) 相似,没有显著差异.

结论:

DL-aMRI可在没有对比剂的情况下进行精确的HCC诊断,大大减少扫描时间.
该协议保留了非对比和对比增强的MRI数据.
DL-aMRI代表了HCC诊断的有价值,高效的工具,具有重要的临床影响.

关键词:

缩写磁共振成像技术缩写磁共振成像技术生成型的人工智能 (GAI) 是一种人工智能.肝细胞癌的诊断肝细胞癌的诊断图像合成图像合成稳定的扩散模型

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