虚拟多相对比增强肝脏MRI使用深度学习来评估肝细胞癌
Yunfei Zhang1,2, Xianling Qian2, Changwu Zhou2
1Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.
一个新的深度学习模型产生多相对比增强核磁共振 (CE-MRI) 用于肝细胞癌 (HCC) 检测. 这种人工智能方法为传统方法提供了可比的图像质量和诊断性能,大大减少了扫描时间并消除了对比剂的需求.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 肝细胞癌 (HCC) 的诊断依赖于对比度增强的MRI (CE-MRI).
- 传统的多相CE-MRI需要大量的时间和基于加多的对比剂 (GBCA).
- 深度学习 (DL) 提供了提高MRI采集效率和安全性的潜力.
研究的目的:
- 开发和评估一个DL模型,用于合成多相CE-MRI.
- 评估DL合成CE-MRI用于HCC检测的图像质量,诊断性能和LI-RADS实用性.
- 将基于DL的CE-MRI的效率和安全性与传统方法进行比较.
主要方法:
- 在717名患有HCC或其他肝脏疾病的患者的CE-MRI数据上训练了一种DL模型.
- 该模型合成了动脉,门静脉,过渡期和肝胆阶段CE-MRI.
- 三位放射科医生评估了图像质量,诊断性能,LI-RADS特征和DL合成的文物与实际的CE-MRI.
主要成果:
- 与实际的CE-MRI相比,DL合成的CE-MRI显示HCC检测的图像质量和诊断性能不劣.
- 对于LI-RADS主要特征,在DL合成和实际CE-MRI之间观察到很好的一致性.
- DL模型在几秒钟内产生了多相CE-MRI,大大减少了采集时间 (0.200.60s vs. >20分钟),并消除了GBCA的使用.
结论:
- DL模型有效地合成了多相CE-MRI,具有高图像质量和HCC的诊断准确性.
- 这种由人工智能驱动的方法可以大大节省时间,消除对比剂的需求,并显示出强度.
- 基于DL的策略显示了临床翻译的巨大潜力,改善了肝脏成像中的患者护理和医疗保健效率.
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