Regulating the development of accurate data-driven physics-informed deformation models

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2024-09-04 DOI:10.1088/2632-2153/ad7192
Will Newman, Jamshid Ghaboussi, Michael Insana
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Abstract

The challenge posed by the inverse problem associated with ultrasonic elasticity imaging is well matched to the capabilities of data-driven solutions. This report describes how data properties and the time sequence by which the data are introduced during training influence deformation-model accuracy and training times. Our goal is to image the elastic modulus of soft linear-elastic media as accurately as possible within a limited volume. To monitor progress during training, we introduce metrics describing convergence rate and stress entropy to guide data acquisition and other timing features. For example, a regularization term in the loss function may be introduced and later removed to speed and stabilize developing deformation models as well as establishing stopping rules for neural-network convergence. Images of a 14.4 cm3 volume within 3D software phantom visually indicate the quality of modulus images resulting over a range of training variables. The results show that a data-driven method constrained by the physics of a deformed solid will lead to quantitively accurate 3D elastic modulus images with minimum artifacts.
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规范准确的数据驱动型物理信息变形模型的开发
与超声波弹性成像相关的逆问题所带来的挑战与数据驱动解决方案的能力非常匹配。本报告介绍了数据属性和在训练过程中引入数据的时间顺序如何影响形变模型的准确性和训练时间。我们的目标是在有限的体积内尽可能精确地对软线性弹性介质的弹性模量进行成像。为了监控训练过程中的进展,我们引入了描述收敛速度和应力熵的指标,以指导数据采集和其他计时特征。例如,可以在损失函数中引入正则化项,之后再将其移除,以加快和稳定变形模型的开发,并建立神经网络收敛的停止规则。三维软件模型中一个 14.4 cm3 体积的图像直观地显示了在一系列训练变量下产生的模量图像的质量。结果表明,受变形实体物理学制约的数据驱动方法可生成定量精确的三维弹性模量图像,并将伪影降到最低。
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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