在DCE-MRI的药理动力学建模中的不确定性估计
Jonas M Van Elburg1, Natalia V Korobova1, Mohammad M Islam2
1Department of Radiology and Nuclear Medicine, University Medical Center, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands.
平均方差估计 (MVE) 神经网络通过提供准确的 perfusion 量化和可靠的不确定性估计来改善动态对比增强型MRI (DCE-MRI),从而提高了对人工智能驱动分析的临床信心.
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:
- 动态对比增强型MRI (DCE-MRI) 可以量化组织输液,但由于噪音数据和复杂的建模,其准确性面临挑战.
- 传统的方法,如非线性最小平方 (NLLS) 拟合产生噪音参数图.
- 深度学习模型提供了更顺的地图,但缺乏可靠的不确定性量化,可能会误导临床医生.
研究的目的:
- 实施和评估DCE-MRI的一组平均方差估计 (MVE) 神经网络.
- 量化 perfusion 参数和相关的 aleatoric 和 epistemic 不确定性.
- 为了比较MVE与NLLS和物理信息神经网络 (PINNs) 的性能,以估计不确定性.
主要方法:
- 开发了一组MVE神经网络,用于DCE-MRI perfusion量化.
- 实现了NLLS和PINNs的基于共变矩阵的常规不确定性估计.
- 将MVE与NLLS和PINNs使用模拟和体内数据进行比较,重点关注 perfusion 精度和不确定性估计.
主要成果:
- 与模拟中的NLLS和PINNs相比,MVE在输液和不确定性估计方面都表现出更高的准确性.
- MVE的定量不确定性与实际错误密切相关,不像NLLS和PINNs,它们往往会高估.
- 在体内,MVE产生了更流,更可靠的不确定性图,特别是在肝脏区域,优于NLLS和PINNs.
结论:
- 通过提供强大的 perfusion 参数和不确定性估计,MVE 增强了定量 DCE-MRI.
- 这种方法提高了人工智能驱动的MRI分析的可靠性,促进了更大的临床信心.
- MVE促进了先进的定量核磁共振技术的临床转化.
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