Adaptive Information Refinement Modeling of Nonlinear Shear Wave Propagation in Biological Tissue

生物组织中非线性剪切波传播的自适应信息细化建模

• 批准号: 1903174
• 负责人: Sorin Mitran
• 金额: $ 56万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903174&HistoricalAwards=false
• 关键词: Adaptive; Information; Refinement; Modeling; Nonlinear

This project will combine high-resolution experiments with recent advances in mathematical information theory to describe the propagation of shear waves in human tissue and simulants, laying the foundation for development of novel medical diagnostic equipment and a better understanding of injuries such as concussion. Wave propagation through biological tissue is a fundamental physical process upon which numerous diagnostic and therapeutic techniques are based. Well-established diagnostic techniques include ultrasound imaging based upon high-frequency sound waves or magnetic resonance imaging based upon radio waves. A new window to investigate biological tissue is the use of mechanical shear waves that travel faster through stiff tissue and slower through soft tissue. By use of shear waves it is possible to effectively carry out palpation of internal organs otherwise accessible only by surgery. However, the propagation of shear waves in biological tissue is not well understood due to the large variability in wave velocity through different tissue types, much larger than that of sound waves. This can lead to counterintuitive effects such as formation of a concussion far from the immediate area of a head blow due to focusing of shear waves reflected by the cranium. Conversely, the large variability in shear wave velocity can be leveraged to carry out detailed investigations of biological tissue outside the capabilities of other diagnostic techniques. To enable such diagnostics there is a need to obtain a fundamental understanding of the relationship between tissue structure and shear wave propagation, as investigated in this project. Project activities include mentoring of graduate student and postdoctoral participants, integration of research results into data-driven modeling courses for undergraduates, as well as presentation of information theory to the general public through outreach activities at the Morehead Planetarium and University of North Carolina Science Days.Strain-stiffening, probably due to microscopic reorganization of filamentary structures in cells and the extracellular matrix, leads to the formation of shear shock waves induced by excitation amplitudes as low as 1% strain. The subsequent wavefront steepening can readily surpass 7% strain leading to tissue injury. The shear modulus can vary by five orders of magnitude in different tissue types and is close to zero in liquid-filled interstitia that exhibit random placement in organs such as the brain. Bulk-homogenized viscoelastic models with a discrete spectrum are inapplicable outside their calibration domain and are not linked by physical principles to the specific tissue. This study pursues a combined experimental-theoretical approach to development of scale-dependent homogenized models of shear wave propagation in highly heterogeneous biological tissue. High-resolution, 1024 channel measurements at 10 kHz and 1 micrometer resolution are used to construct detailed images of shear wave propagation through biological tissue and phantoms. A multiple-scale computational model is constructed that uses information-theoretic concepts such as mutual information to link the different simulation levels, offering a new perspective on and development of traditional adaptive mesh and model refinement. Results from this investigation will be generally applicable to nonlinear, multiscale stochastic systems that do not exhibit scale separation. Both experimental results and computational code will be available through literate programming and reproducible research practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将联合收割机与数学信息论的最新进展相结合，描述剪切波在人体组织和模拟物中的传播，为开发新型医疗诊断设备和更好地了解脑震荡等损伤奠定基础。波通过生物组织的传播是一个基本的物理过程，许多诊断和治疗技术都是基于此。完善的诊断技术包括基于高频声波的超声成像或基于无线电波的磁共振成像。研究生物组织的一个新窗口是使用机械剪切波，其通过硬组织的速度更快，通过软组织的速度更慢。通过使用剪切波，可以有效地对内部器官进行触诊，否则只能通过手术才能到达。然而，剪切波在生物组织中的传播并没有得到很好的理解，这是由于通过不同组织类型的波速的大的可变性，比声波的大得多。这可能导致违反直觉的效果，例如由于颅骨反射的剪切波的聚焦而在远离头部撞击的直接区域形成脑震荡。相反，剪切波速度的大的可变性可以被利用来进行其他诊断技术能力之外的生物组织的详细调查。为了实现这样的诊断，有必要获得组织结构和剪切波传播之间的关系的基本理解，在这个项目中的调查。项目活动包括指导研究生和博士后参与者，将研究成果整合到本科生的数据驱动建模课程中，以及通过莫尔黑德天文馆和北卡罗来纳州大学科学日的外联活动向公众介绍信息理论。应变硬化，可能是由于细胞和细胞外基质中的细胞膜结构的微观重组，导致由低至1%应变的激励振幅诱导的剪切冲击波的形成。随后的波前陡化可以容易地超过7%的应变，导致组织损伤。在不同的组织类型中，剪切模量可以变化五个数量级，并且在充满液体的动脉中接近于零，这些动脉在诸如大脑的器官中表现出随机放置。具有离散谱的体均质化粘弹性模型在其校准域之外是不适用的，并且不通过物理原理与特定组织相关联。 本研究采用实验与理论相结合的方法，发展了剪切波在高度异质性生物组织中传播的尺度相关均质化模型。高分辨率，1024通道测量在10 kHz和1微米的分辨率被用来构建通过生物组织和体模的剪切波传播的详细图像。一个多尺度的计算模型的构建，使用信息论的概念，如互信息链接不同的仿真水平，提供了一个新的视角和发展传统的自适应网格和模型细化。这项调查的结果将普遍适用于非线性，多尺度随机系统，不表现出规模分离。实验结果和计算代码都将通过识字编程和可重复的研究实践来提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。