通过卷积神经网络通过扫描激光多普勒振量测量估计异型材料的剪切模量
Silvia Leccabue1, Sara Moccia2, Thomas J Royston3
1University of Illinois Chicago, 1200 West Harrison Street, Chicago, 60607, IL, USA; Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy.
概括
这项研究使用扫描激光多普勒振动计 (SLDV) 和卷积神经网络 (CNN) 来估计材料刚度. 该方法在二元分类中达到84.4%的准确率,在基于的材料的多类分类中达到76.6%.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 医疗成像医学成像
背景情况:
- 组织机械特性是对病理变化敏感的关键诊断参数.
- 扫描激光多普勒振动计 (SLDV) 是一种非接触式,非侵入性的动态弹性成像技术.
- 卷积神经网络 (CNN) 显示出从弹性图像中准确评估机械性能的潜力.
研究的目的:
- 探索SLDV和CNN的联合使用,以估计基于的材料的刚性.
- 开发和评估CNN架构,使用弹性图数据进行机械性能分类.
- 评估软组织模拟材料中自动化刚度估计的可行性.
主要方法:
- 利用模仿软组织的材料来模拟现实的变形和刚性.
- 采用两种不同的机械振动源在SLDV成像过程中刺激标本.
- 开发了一个CNN架构,在合成数据上进行预训练,并在物理SLDV数据上进行微调,用于剪切模量分类.
主要成果:
- 实现了84.4%的准确度,用于对剪切模量进行二进制分类.
- 报道了76.6%的准确性,用于将剪切模量分为五个类别的分类.
- 证明了用合成数据预训练CNN的有效性,以微调物理数据.
结论:
- 这项研究为从弹性图像中自动和可靠地评估机械性能迈出了一大有希望的步骤.
- 虽然该方法尚未在临床上适用于器官度估计,但该方法显示了未来诊断工具的潜力.
- SLDV和CNN的整合为材料刚性表征提供了一种新的方法.
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