Fast Adaptive Mesh Augmented Lagrangian Digital Image Correlation

Yang, Jin; Bhattacharya, Kaushik

doi:10.1007/s11340-021-00695-9

Cited by 14 publications

(6 citation statements)

References 50 publications

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“…However, this is inevitable because some noise and the resulting errors cannot be avoided but only minimised. A recently published study [ 41 ] compared different DIC approaches including local and global ones but also a newly proposed augmented Lagrangian DIC method which includes mesh adaptability.…”

Section: Discussionmentioning

confidence: 99%

“…While most of the samples presented there simulate only rigid motion with varying levels of noise or distortion, there are cases (like sample 14 in a previous study [40] ) introducing sub-pixel heterogeneous deformation and thus a strain field representing a real challenge for the algorithms, as stated by the authors. This sample is very often used in testing new modifications of DIC [41,42] and hence it was chosen also here to challenge the IR approach. As it is suited for DIC, it has a typical speckle pattern though with high noise, introducing uncertainties into the analysed displacements.…”

Section: Synthetic Data: Heterogeneous Deformationsmentioning

confidence: 99%

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Evaluation of image registration for measuring deformation fields in soft tissue mechanics

Lisický

Avril

Eydan

et al. 2022

Strain

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High‐fidelity biomechanical models usually involve the mechanical characterisation of biological tissues using experimental methods based on optical measurements. In most experiments, strains are evaluated based on displacements of a few markers and represents an average within the region of interest (ROI). Full‐field measurements may improve description of non‐homogeneous materials such as soft tissues. The approach based on non‐rigid image registration is proposed and compared with standard digital image correlation (DIC) on a set of samples, including (i) complex heterogeneous deformations with sub‐pixel displacement, (ii) a typical uniaxial tension test of aorta, and (iii) an indentation test on skin. The possibility to extend the ROI to the whole sample and the exploitation of a natural tissue pattern represents the main assets of the proposed method whereas the results show similar accuracy as standard DIC when analysing sub‐pixel deformations. Therefore, displacement and strain fields measurement based on image registration is very promising to characterise heterogeneous specimens with irregular shapes and/or small dimensions, which are typical features of soft biological tissues.

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Section: Discussionmentioning

confidence: 99%

Section: Synthetic Data: Heterogeneous Deformationsmentioning

confidence: 99%

Evaluation of image registration for measuring deformation fields in soft tissue mechanics

Lisický

Avril

Eydan

et al. 2022

Strain

View full text Add to dashboard Cite

show abstract

“…In a broader sense, capturing full-field soft tissue deformation is critical for a number of research applications ranging from inverse material characterization [ 54 – 57 ], to high-fidelity biomechanical modeling of in vivo mechanisms [ 58 – 60 ], and patient-specific modeling and procedure planning [ 61 , 62 ]. And recently, there has been keen interest in implementing image registration-based techniques widely used in the field of computer vision [ 54 , 63 ] for full-field measurements, including digital image correlation (DIC) [ 64 – 68 ], 3D-DIC [ 69 , 70 ], digital volume correlation (DVC) [ 71 – 74 ], and optical flow algorithms [ 75 – 77 ].…”

Section: Methodsmentioning

confidence: 99%

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

Kobeissi,

Jilberto,

Karakan

et al. 2024

PLoS ONE

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Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present “MicroBundleCompute,” a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate “MicroBundleCompute” as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.

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“…DIC is a powerful full-field measurement tool to analyze local displacement and strain distribution [14]. Many advanced DIC techniques like q-factorbased DIC (qDIC) [55] and augmented-Lagrangian DIC (ALDIC) [56,57] were developed to increase the accuracy, efficiency, and robustness of strain field calculation. Recently, Yang et al [58] showed the pre-trained CNN-based DL algorithms from synthetic data can accurately predict end-to-end measurement of displacement and strain fields from experimental speckle images.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

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Fast Adaptive Mesh Augmented Lagrangian Digital Image Correlation

Cited by 14 publications

References 50 publications

Evaluation of image registration for measuring deformation fields in soft tissue mechanics

Evaluation of image registration for measuring deformation fields in soft tissue mechanics

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

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