统一贝叶斯网络用于动态对比增强 (DCE) 肝脏MRI生理参数的不确定性量化
Edengenet M Dejene1,2, Winfried Brenner3, Marcus R Makowski2,4
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.
Physics in medicine and biology
|October 11, 2023
概括
使用贝叶斯神经网络 (BNNs) 的新型深度学习框架改善了动态对比增强MRI (DCE-MRI) 肝脏的参数和不确定性估计. 这种方法比传统方法提供了更准确的结果,特别是在杂的或分布之外的场景中.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 量化MRI是指数量化的MRI.
背景情况:
- 在DCE-MRI中,生理参数估计受到数据噪声和模型不准确性的挑战.
- 准确的参数和不确定性量化对于可靠的DCE-MRI临床解释至关重要.
研究的目的:
- 开发一个深度学习框架,用于肝脏DCE-MRI中精确的参数和不确定性估计.
- 用贝叶斯神经网络 (BNN) 来解决内部数据模糊性,如噪音和模型不准确性.
主要方法:
- 模拟的度时间曲线被用于训练BNN.
- 在BNN培训最小化了损失函数,包括 aleatoric 和 epistemic 不确定性.
- 性能与使用模拟和体内肝脏瘤数据的非线性最小平方 (NLLS) 拟合进行了比较.
主要成果:
- 与NLLS相比,BNN在各种噪音水平上显示了较低的根平均平方误差 (RMSE).
- 对于体内数据,BNN实现了显著更强大的参数估计,区分健康和瘤组织 (p<0.0001).
- 来自BNN的不确定性估计有效地确定了高噪音和分布之外的数据场景.
结论:
- 拟议的BNN框架允许准确的定量DCE-MRI参数估计.
- 来自BNN的不确定性估计为数据质量和模型在临床环境中的适用性提供了关键的见解.
- 这种方法通过标记模型性能不足,指导进一步的数据采集或模型适应来提高DCE-MRI的可靠性.
关键词:
贝叶斯神经网络是一个贝叶斯神经网络.在DCEMRI中,DCEMRI是指DCE的MRI.肝脏 perfusion perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion 肝脏 perfusion参数估计的参数估计.定量成像技术 定量成像技术追踪器动态建模的追踪器不确定性量化不确定性量化
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