scholarly journals Predictive constitutive modelling of arteries by deep learning

2021 ◽  
Vol 18 (182) ◽  
pp. 20210411
Author(s):  
Gerhard A. Holzapfel ◽  
Kevin Linka ◽  
Selda Sherifova ◽  
Christian J. Cyron
Keyword(s):  
Mechanical Properties ◽  
Deep Learning ◽  
Second Harmonic ◽  
Biological Tissues ◽  
Constitutive Modelling ◽  
Coefficient Of Determination ◽  
Soft Biological Tissues ◽  
Tissue Samples ◽  
Hybrid Modelling ◽  
Modelling Framework

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R 2 = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.

scholarly journals An automated approach for three-dimensional quantification of fibrillar structures in optically cleared soft biological tissues

2013 ◽  
Vol 10 (80) ◽  
pp. 20120760 ◽  
Author(s):  
Andreas J. Schriefl ◽  
Heimo Wolinski ◽  
Peter Regitnig ◽  
Sepp D. Kohlwein ◽  
Gerhard A. Holzapfel
Keyword(s):  
Second Harmonic ◽  
Structural Parameters ◽  
Three Dimensional ◽  
Collagen Fibre ◽  
Likelihood Estimation ◽  
Biological Tissues ◽  
Distribution Fitting ◽  
Soft Biological Tissues ◽  
Tissue Samples ◽  
Novel Approach

We present a novel approach allowing for a simple, fast and automated morphological analysis of three-dimensional image stacks ( z -stacks) featuring fibrillar structures from optically cleared soft biological tissues. Five non-atherosclerotic tissue samples from human abdominal aortas were used to outline the multi-purpose methodology, applicable to various tissue types. It yields a three-dimensional orientational distribution of relative amplitudes, representing the original collagen fibre morphology, identifies regions of isotropy where no preferred fibre orientations are observed and determines structural parameters throughout anisotropic regions for the analysis and numerical modelling of biomechanical quantities such as stress and strain. Our method combines optical tissue clearing with second-harmonic generation imaging, Fourier-based image analysis and maximum-likelihood estimation for distribution fitting. With a new sample preparation method for arteries, we present, for the first time to our knowledge, a continuous three-dimensional distribution of collagen fibres throughout the entire thickness of the aortic wall, revealing novel structural and organizational insights into the three arterial layers.

scholarly journals A Deep Learning Approach to Estimate Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images

2017 ◽  
Author(s):  
Liang Liang ◽  
Minliang Liu ◽  
Wei Sun
Keyword(s):  
Mechanical Properties ◽  
Deep Learning ◽  
Elastic Properties ◽  
Second Harmonic ◽  
Learning Approach ◽  
Stress Strain ◽  
Tissue Samples ◽  
Anisotropic Stress ◽  
Collagenous Tissue ◽  
Microscopy Images

ABSTRACTBiological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen as a representative collagenous tissue. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images.

scholarly journals On fibre dispersion modelling of soft biological tissues: a review

2019 ◽  
Vol 475 (2224) ◽  
pp. 20180736 ◽  
Author(s):  
Gerhard A. Holzapfel ◽  
Ray W. Ogden ◽  
Selda Sherifova
Keyword(s):  
Heart Valves ◽  
Mechanical Response ◽  
Structural Information ◽  
Second Harmonic ◽  
Biological Tissues ◽  
Mechanical Tests ◽  
Soft Biological Tissues ◽  
Tensor Models ◽  
Continuum Modelling ◽  
Stress And Deformation

Collagen fibres within fibrous soft biological tissues such as artery walls, cartilage, myocardiums, corneas and heart valves are responsible for their anisotropic mechanical behaviour. It has recently been recognized that the dispersed orientation of these fibres has a significant effect on the mechanical response of the tissues. Modelling of the dispersed structure is important for the prediction of the stress and deformation characteristics in (patho)physiological tissues under various loading conditions. This paper provides a timely and critical review of the continuum modelling of fibre dispersion, specifically, the angular integration and the generalized structure tensor models. The models are used in representative numerical examples to fit sets of experimental data that have been obtained from mechanical tests and fibre structural information from second-harmonic imaging. In particular, patches of healthy and diseased aortic tissues are investigated, and it is shown that the predictions of the models fit very well with the data. It is straightforward to use the models described herein within a finite-element framework, which will enable more realistic (and clinically relevant) boundary-value problems to be solved. This also provides a basis for further developments of material models and points to the need for additional mechanical and microstructural data that can inform further advances in the material modelling.

Rheological Properties of Anisotropic Tissues at Large Amplitude Oscillatory Shear

2012 ◽  
Author(s):  
Kristy Tan ◽  
Shaokoon Cheng ◽  
Lynne E. Bilston
Keyword(s):  
Mechanical Properties ◽  
Load Transfer ◽  
Biological Tissues ◽  
The Body ◽  
Accurate Information ◽  
Soft Biological Tissues ◽  
The Past ◽  
Crash Testing ◽  
Car Crash ◽  
The Brain

The mechanical properties of soft biological tissues have been widely investigated over the past five decades [1–5]. Reported measurements of soft biological tissues such as the brain, spinal cord, liver and muscle vary by orders of magnitude, depending on the sample preparation, anisotropy and loading regime. Knowing the accurate mechanical properties of biological tissues is important for many applications, for example car crash testing and simulations require accurate information on how different parts of the body deform due to a combination of loads. Deformation of tissues around prosthetics and artificial limbs are critical in understanding load transfer at interfaces with the body. The recent use of Magnetic Resonance Elastography (MRE) in diagnostic imaging has resulted in a surge of interest in accurate measurements of mechanical properties of tissues [6].

scholarly journals Electronic devices for studying mechanical properties of biological tissues

2020 ◽  
pp. 40-47
Author(s):  
A. G. Dubko ◽  
R. S. Osipov ◽  
Yu. V. Bondarenko ◽  
O. F. Bondarenko
Keyword(s):  
Mechanical Properties ◽  
Mathematical Models ◽  
Biological Tissue ◽  
Low Cost ◽  
Operation Mode ◽  
Experimental System ◽  
Biological Tissues ◽  
Optimal Operation ◽  
Biocompatible Materials ◽  
Tissue Samples

The paper shows the relevance of studying the mechanical properties of biological tissues and biocompatible materials for solving the problems of general and reconstructive surgery, transplantology, manual therapy, virtual simulation of surgical operations, robotic surgery, etc. The authors present basic information about biological tissue as an object of research and give a brief overview of the devices used for studying the mechanical characteristics of biological tissues. An experimental system for testing deformations of biological tissues and biocompatible materials during compression is described. The system is developed using modern hardware and software, as well as effective technical solutions. The results of the practical use of the developed device are presented and the obtained dependences of the mechanical stress of biological tissue samples on their deformation under pressure are analyzed. The system has high metrological characteristics and low cost, and allows performing all the necessary functions for measuring, processing and visualizing the data. The measurements obtained with this system can help form the recommendations for doctors on choosing the optimal operation mode of medical devices and instruments in each specific case. In addition, the measured data can be used to create mathematical models of biological tissues and biocompatible materials in order to further carry out virtual experiments. In further studies, the authors plan to create the mathematical models of biological tissues based on the finite element method and using the actual values characterizing the tissue, obtained with the developed system.

Structurally Motivated Models of the Arterial Wall Tissue

2013 ◽  
Vol 05 (04) ◽  
pp. 1330002 ◽  
Author(s):  
Hadi Taghizadeh ◽  
Mohammad Tafazzoli Shadpour
Keyword(s):  
Mechanical Properties ◽  
Blood Pressure ◽  
Arterial Wall ◽  
Mechanical Characteristics ◽  
Biological Tissues ◽  
Soft Biological Tissues ◽  
Mechanical Environment ◽  
Highly Sensitive ◽  
Mechanical Models ◽  
Mechanical Factors

Mechanical characteristics of soft biological tissues mostly depend on their hierarchy at different scales from nano- to macro-structure. It is shown that arterial wall tissue is highly sensitive to its mechanical environment and any alteration in mechanical factors such as blood pressure, triggers physio- pathological processes within arterial wall. Quantification of these mechanical properties will provide us with deeper insights of ongoing biological events. In this context, mechanical contributions of wall constituents in health and diseases are of growing interest. Hence, this review is concerned with mechanical models of arterial wall tissue with a focus on microstructurally motivated representations.

scholarly journals Protein Hydrogels: The Swiss Army Knife for Enhanced Mechanical and Bioactive Properties of Biomaterials

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1656
Author(s):  
Carla Huerta-López ◽  
Jorge Alegre-Cebollada
Keyword(s):  
Mechanical Properties ◽  
Tissue Engineering ◽  
Polymer Networks ◽  
Biological Tissues ◽  
Modern Medicine ◽  
Body Parts ◽  
Biomedical Sciences ◽  
Protein Hydrogels ◽  
Large Water ◽  
Design Options

Biomaterials are dynamic tools with many applications: from the primitive use of bone and wood in the replacement of lost limbs and body parts, to the refined involvement of smart and responsive biomaterials in modern medicine and biomedical sciences. Hydrogels constitute a subtype of biomaterials built from water-swollen polymer networks. Their large water content and soft mechanical properties are highly similar to most biological tissues, making them ideal for tissue engineering and biomedical applications. The mechanical properties of hydrogels and their modulation have attracted a lot of attention from the field of mechanobiology. Protein-based hydrogels are becoming increasingly attractive due to their endless design options and array of functionalities, as well as their responsiveness to stimuli. Furthermore, just like the extracellular matrix, they are inherently viscoelastic in part due to mechanical unfolding/refolding transitions of folded protein domains. This review summarizes different natural and engineered protein hydrogels focusing on different strategies followed to modulate their mechanical properties. Applications of mechanically tunable protein-based hydrogels in drug delivery, tissue engineering and mechanobiology are discussed.

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