基于深度学习和计算流体动力学的儿科水性的非侵入性尿流动力学特征
Mingjing Yang1, Zhanpeng Zhu1, Liqin Huang1
1College of Physics and Information Engineering, Fuzhou University, No. 2 Wulongjiang North Road, Fuzhou, 350108, Fujian, China.
Computer methods and programs in biomedicine
|October 5, 2025
概括
这项研究引入了一种使用深度学习和计算流体动力学 (CFD) 分析儿科水流动力学的非侵入性方法,为评估阻塞和指导治疗提供了一种新方法.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 计算流体动力学的流体动力学.
背景情况:
- 儿科水性,通常是由尿管盆结阻塞 (UPJO) 引起的,是一种常见的疾病.
- 非侵入性磁共振泌尿学 (MRU) 可视化盆和囊,但缺乏详细的压力和速度测量.
- 目前用于评估UPJO的侵入性方法通常对患者来说是不舒服的.
研究的目的:
- 开发一种使用深度学习和CFD计算尿液流动力学的非侵入性方法.
- 为了获得有关水症患者泌尿动力学特征的见解,以改善临床决策.
- 为UPJO诊断和预后提供一个工具.
主要方法:
- 用MRI数据进行深度学习,重建患者特有的3D骨盆模型.
- 计算流体动力学 (CFD) 模拟尿液流动使用粘性k-epsilon流模型.
- 尿流动动态的特征,用于UPJO的评估.
主要成果:
- 与手术前相比,手术后的骨压力明显降低.
- 引入了尿液流速比率 (UFVR) 指数来量化骨盆尿液阻塞.
- 经过手术干预后,显示了尿流阻塞的缓解.
结论:
- CFD模拟可以预测脏盆腔压力非侵入性,避免像惠特克测试这样的痛苦测试.
- 数字模拟提供了一种明确的方法来评估皮整形手术的有效性.
- 开发的方法增强了UPJO和治疗结果的非侵入性评估.
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