动态对比增强 (DCE) MRI估计血管参数使用基于知识的自适应模型
Hassan Bagher-Ebadian1,2,3,4, Stephen L Brown5,6,7, Mohammad M Ghassemi8
1Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA. hbagher1@hfhs.org.
四个自适应模型 (AMs) 从没有动脉输入功能的动态对比增强MRI估计了微血管参数. 与传统方法相比,这种方法提供了稳定,改进的瘤和正常组织微血管的量化.
科学领域:
- 医疗成像医学成像
- 生物物理学的生物物理.
- 药理动力学 药理动力学
背景情况:
- 动态对比增强 (DCE) MRI对于评估微血管参数至关重要.
- 传统的DCE-MRI分析通常依赖于动脉输入函数 (AIF),其准确获得可能具有挑战性.
- 精确估计微血管参数,如Ktrans,VP和VE对于理解组织生理学和疾病至关重要.
研究的目的:
- 引入和验证四种自适应模型 (AMs) 来估计DCE-MRI数据的微血管参数.
- 在不需要AIF的情况下,进行基于生理学的嵌套模型选择 (NMS) 估计.
- 为了提高DCE-MRI基于微血管的定量化的稳定性和准确性.
主要方法:
- 使用嵌套交叉验证 (NCV) 方法开发和验证四个AM.
- 从DCE-MRI原始数据中提取190个特征来构建AMs.
- 在66只大鼠中应用扩展的基于Patlak的NMS范式和基于NMS的先验知识来进行参数估计.
主要成果:
- 与传统分析相比,AMs产生了血管参数和嵌套模型区域的稳定地图,与AIF分散相比,AIF分散的影响较小.
- 实现了高绩效指标:地区为0.914/0.834,VP为0.825/0.720,Ktrans为0.938/0.880,ve为0.890/0.792 (相关系数/调整后R平方).
- 附属医院证明了微血管性质的量化改进.
结论:
- 适应模型提供了一个强大的方法,用于DCE-MRI基于微血管量化没有AIF.
- 这种方法可以加快和提高DCE-MRI分析对瘤和正常组织的准确性.
- 经过验证的AM代表了与传统的DCE-MRI分析技术相比的重大进步.
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