A Novel Diagnostic Tool: from Structural Health Monitoring to Tissue Quality Prediction

一种新型诊断工具：从结构健康监测到组织质量预测

• 批准号: EP/K036939/1
• 负责人: K Chen
• 金额: $ 130.5万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK036939%2F1
• 关键词: Novel; Diagnostic; Tool; Structural; Health

As quality of life constantly improves, the average lifespan will continue to increase. The bad news is that tissue degradation due to wear and tear in an aged body is inevitable and is different from person to person. Fortunately recent advances in sciences and technology have enabled us to work towards personalised medicine this approach calls for a collective effort of researchers from a vast spectrum of specialised subjects. This project, by an interdisciplinary team from four different UK Universities with distinct areas of expertise, aims to predict patient-specific tissue quality which is essential in devising treatments plans. While our primary concern in this study is the bone tissue, the developed framework will apply to other tissues having porous or complex microstructure.To achieve the aim of the project, we have to overcome a few challenges. Firstly we need better mathematical models to extract the tissue microstructure accurately and automatically and also reliable mathematical methods for comparing two different samples from either a normal image (say with 1024 x 1024 pixels) or a 3D image containing shapes and embedded complex structures. Although one would expect that existing softwares can do these tasks, the reality is that these tasks are harder than we think as the images from a realistic scan contains noise. To counter noise, we use a novel mathematical technique called the variational method that actually constructs an energy quantity involving the whole image and minimises it. In doing so, a noise-free reconstruction is obtained and the precise location of the underlying tissue is identified. This project will develop efficient and robust models to extract local features only. Similarly we can construct different energies to compare two images in the so-called co-registration problem. Our new idea is to use more mathematical approaches and less statistical estimation ideas to achieve more accuracy and robustness. The CMIT research centre at Liverpool specialises in a range of mathematical models for different problem scenarios. Secondly once imaging extracts microstructural geometry, our team from Edinburgh and Heriot-Watt will develop and use a computational mechanics approach to evaluate the tissues' material properties. Since tissue quality depends on what its microstructure looks like, answers to questions such as: is it very porous, are different solid parts poorly connected and are most of the pores aligned in the same direction, should indicate how strong the tissue is. We will examine these microstructures and then conduct range of mechanical tests on the computer so as to obtain properties that tell us when and how the tissue will get damaged or fail. We then study how the microstructural geometry relates to the mechanical behaviour. We will use a process called homogenisation that will enable prediction of properties at macro (tissue) level from knowledge of micro level. Homogenisation to predict tissue failure has not been attempted before and presents a major challenge. Finally, the predicted properties must be validated from a combination of imaging and bespoke in-vitro experimentation. The validation process, to be done in Durham, will evaluate the accuracy of our models and provide model refinements. Once validated, our methodology can be used with confidence as a tool to evaluate tissue properties straight from images.Thus the proposed project contains both technical and advanced mathematical components and real applications in scenarios where non-invasive imaging is applicable. With imaging technology getting better, cheaper and more wide-spread, the prospects for novel applications of this work are immense.

随着生活质量的不断提高，人们的平均寿命将继续增加。坏消息是，由于老化的身体磨损导致的组织退化是不可避免的，而且因人而异。幸运的是，最近科学技术的进步使我们能够致力于个性化医学，这种方法需要来自广泛专业领域的研究人员的集体努力。该项目由来自四所不同英国大学的跨学科团队进行，他们拥有不同的专业领域，旨在预测患者特定的组织质量，这在设计治疗计划时至关重要。虽然我们在这项研究中主要关注的是骨组织，但所开发的框架将适用于其他具有多孔或复杂微结构的组织。为了实现该项目的目标，我们必须克服一些挑战。首先，我们需要更好的数学模型来准确和自动地提取组织微结构，还需要可靠的数学方法来比较来自正常图像(例如1024 x 1024像素)或包含形状和嵌入复杂结构的3D图像的两个不同样本。尽管人们预计现有的软件可以完成这些任务，但现实情况是，这些任务比我们想象的要困难，因为来自真实扫描的图像包含噪声。为了应对噪声，我们使用了一种新的数学技术，称为变分方法，它实际上构造了一个涉及整个图像的能量值，并将其最小化。在这样做的过程中，获得了无噪声的重建，并且识别了下面组织的准确位置。该项目将开发高效和健壮的模型来提取局部特征。类似地，在所谓的联合配准问题中，我们可以构造不同的能量来比较两幅图像。我们的新想法是使用更多的数学方法和更少的统计估计思想来实现更高的准确性和稳健性。利物浦的CMIT研究中心专门研究针对不同问题情景的一系列数学模型。其次，一旦成像提取出微结构几何，我们来自爱丁堡和赫里奥特-瓦特的团队将开发并使用计算力学方法来评估组织的材料特性。由于组织的质量取决于其微观结构的样子，因此对以下问题的回答应该表明组织的强度：它是否非常疏松，不同的固体部分是否连接不良，以及大多数毛孔是否沿同一方向排列。我们将检查这些微观结构，然后在计算机上进行一系列机械测试，以获得告诉我们组织何时以及如何受损或失效的特性。然后，我们研究了微结构几何与力学行为之间的关系。我们将使用一种称为均质化的过程，这将使我们能够从微观层面的知识预测宏观(组织)层面的特性。预测组织衰竭的均质化以前从未尝试过，这是一个重大挑战。最后，预测的特性必须通过成像和定制的体外实验相结合来验证。验证过程将在达勒姆进行，将评估我们模型的准确性并提供模型改进。一旦得到验证，我们的方法可以自信地用作直接从图像评估组织属性的工具。因此，拟议的项目包含技术和高级数学组件，以及在非侵入性成像适用的场景中的实际应用。随着成像技术越来越好、越来越便宜、越来越广泛，这项工作的新应用前景是巨大的。

项目成果

Computational homogenization of histological microstructures in human prostate tissue: Heterogeneity, anisotropy and tension-compression asymmetry

• DOI: 10.1002/cnm.3758
• 发表时间: 2023-07-21
• 期刊: INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING
• 影响因子: 2.1
• 作者: Anderson，Calum;Ntala，Chara;Chen，Yuhang
• 通讯作者: Chen，Yuhang

Mechanical-Stress-Related Epigenetic Regulation of ZIC1 Transcription Factor in the Etiology of Postmenopausal Osteoporosis.

ZIC1 转录因子在绝经后骨质疏松症病因学中的机械应激相关表观遗传调控。

• DOI: 10.17863/cam.82316
• 发表时间: 2022
• 作者: Datta H

An algorithm to map elastic constants in the human femur

绘制人类股骨弹性常数的算法

• 发表时间: 2014
• 作者: Florencio FL

Improved optimization methods for image registration problems

• DOI: 10.1007/s11075-018-0486-2
• 发表时间: 2018-02
• 期刊: Numerical Algorithms
• 影响因子: 2.1
• 作者: Ke Chen;G. N. Grapiglia;Jinyun Yuan;Daoping Zhang

Iterative Constrained Minimization for Vectorial TV Image Deblurring

• DOI: 10.1007/s10851-015-0599-3
• 发表时间: 2016-02-01
• 期刊: JOURNAL OF MATHEMATICAL IMAGING AND VISION
• 影响因子: 2
• 作者: Chen, K.;Piccolomini, E. Loli;Zama, F.
• 通讯作者: Zama, F.