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使用药理动力学信息的深度学习对临床腹部DCE-MRI的回顾性量化:一个概念验证研究

Chaowei Wu^1,2, Nan Wang^1,3, Srinivas Gaddam⁴

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

Frontiers in radiology

|September 21, 2023

概括

深度学习通过改善时间分辨率来增强动态对比增强MRI (DCE-MRI),以更好地评估癌症. 该方法允许对药物动力学参数进行定量分析,有助于客观评估胰腺疾病.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

动态对比增强型MRI (DCE-MRI) 对于癌症的检测,分期和监测至关重要.
传统的多相腹部DCE-MRI在时间分辨率和组织血管性的定量评估方面存在局限性.

研究的目的:

通过使用药理动力学信息的深度学习来研究以后期量化多相腹部DCE-MRI的可行性.
改善DCE-MRI的时间分辨率,以更准确地评估癌症.

主要方法:

合成了30秒时间分辨率的多相DCE-MRI,来自45个受试者 (健康者,PDAC,CP) 的2秒定量DCE序列.
训练了一个药理动力学信息的神经网络,在药理动力学参数量化之前增强时间分辨率.
通过十倍交叉验证评估深度学习估计和参考药理动力学参数之间的一致性.

主要成果:

通过深度学习估计的药理动力学参数与参考值高度一致 (ICC和R2:0.84-0.94).
对于估计参数,观察到较低的变化系数 (5.6%-12.3%).
在健康的胰腺,PDAC瘤和慢性胰腺炎之间发现了显著的参数差异.

结论:

使用深度学习,可以对临床多相DCE-MRI进行追溯量化 (RoQ).
这种技术可以从临床DCE-MRI数据中推导出定量药理动力学参数.
通过深度学习增强的DCE-MRI为更客观和精确的癌症评估提供了潜力.

关键词:

在腹部,腹部,腹部.深度学习是一种深度学习.动态对比增强MRI的动态对比增强药物动力学模型药物动力学模型定量成像技术定量成像技术

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