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肝脏MRI质子密度脂肪分数推断从使用深度学习的对比增强CT图像:一项概念验证研究

Md Nasir¹, Yixi Xu¹, Kyle Hasenstab²

¹AI for Good Lab, Microsoft, Redmond, Washington, United States of America.

|August 8, 2025

概括

这项研究表明,深度学习可以从CT扫描中估计肝脂肪,有助于早期诊断与代谢功能障碍相关的脂肪性肝病 (MASLD). 虽然不精确,但它准确地分类了较低的脂肪等级,提高了MASLD评估的可访问性.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
肝病学肝病学是一种肝病学.

背景情况:

与代谢功能障碍相关的脂肪性肝病 (MASLD) 是一个普遍存在的全球健康问题.
准确的,非侵入性的肝脂肪量化对于早期诊断和管理至关重要.
目前的黄金标准,MRI质子密度脂肪分数 (PDFF),面临着可访问性和成本限制.

研究的目的:

探索使用深度学习从对比增强计算机断层扫描 (CECT) 推断肝脏MRI-PDFF值的可行性.
开发和验证一个深度神经网络 (DNN) 来估计CECT图像中的肝脏脂肪含量.
为了评估DNN预测PDFF与MRI-PDFF基准真理的准确性.

主要方法:

对94名活着的肝脏供体候选人的回顾性分析,同时进行CECT和MRI-PDFF.
使用CECT作为输入和MRI-PDFF作为输出,训练了一个3D U-Net深度神经网络.
使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和卡帕统计数据来评估性能.

主要成果:

在将预测与参考MRI-PDFF中位数进行比较时,DNN模型实现了2.94的MAE和4.27的RMSE.
在DNN预测和基本真相分类肥胖症等级之间发现了实质性的一致性 (kappa = 0.75).
该模型证明了较低级别脂肪酸的强有力的分类分类,优于先前的方法.

结论:

深度学习在估计CECT的肝脏脂肪方面表现有前途,为MRI-PDFF提供了更容易获得的替代方案.
虽然确切的PDFF值是有限的,但分类脂肪等级分类是可靠的,特别是在较低等级.
这种方法可以通过利用易于获得的CECT数据来改善早期MASLD诊断和患者护理.

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