Introduction

Lai, W. Michael; Rubin, David T.; Krempl, E.

doi:10.1016/b978-0-7506-8560-3.00001-3

Cited by 68 publications

(42 citation statements)

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“…Following the cantilever's bending deflection equation, (38) when a force F is applied at the endpoint and perpendicular to the cantilever, as illustrated in Fig. 1, a point (x, y) on the cantilever has the x-axial displacement u and the y-axial displacement v as follows:…”

Section: Displacement Calculation Of Feature Point Based On Dic Methodsmentioning

confidence: 99%

Measurement of Deformation and Force for Microcantilevers Based on Feature Point Matching and Digital Image Correlation

2015

Sensors and Materials

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Key words: microcantilever, micro force measurement, feature point matching, affine invariant feature descriptor, digital image correlationThe measurement of deformation and force for microcantilevers is an important issue in microassembly. An optical method based on local feature point matching and the digital image correlation (DIC) for deformation and force measurement of microcantilevers is proposed. Images of a microcantilever before and after deformation are obtained by an optical microscope. Using the Laplace-of-Gaussian (LOG) operator, feature points with local extreme response values are detected in the scale space in images before and after deformation. For each LOG feature point, an affine-invariant closed region is extracted based on which the sub-pixel position of the LOG point is relocated as the center of gravity of the closed region and an affine-invariant feature descriptor is constructed. Corresponding points in images before and after deformation are matched according to their affine invariant descriptors. To obtain the displacement of feature points with a higher resolution in bending deformation, the digital image correlation algorithm with a third-order transformation function is used and initialized by the sub-pixel information of matched points. The deformation of the microcantilever can be calculated by fitting the displacement of the matching points with the beam's bending deflection formula under the plane stress condition. Then, the force values as well as the position where the loader comes in contact with the cantilever are calculated through the least-squares method. The validity of the proposed method is verified through experiments on actual microcantilever deformation and comparison with other methods.

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Section: Displacement Calculation Of Feature Point Based On Dic Methodsmentioning

confidence: 99%

Measurement of Deformation and Force for Microcantilevers Based on Feature Point Matching and Digital Image Correlation

2015

Sensors and Materials

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“…Under these assumptions we conclude the relation between stress (σ) and strain (ϵ) to be given by (2) [2].…”

Section: A) Stress-strain Relationship Of the Tissue Is Linear B) Thmentioning

confidence: 99%

“…Many assumptions are proposed in that field (elasticity imaging field) to simplify the interpretation and the analysis of images obtained. The most common assumptions for the tissue material are [1,2]:…”

Section: Introductionmentioning

confidence: 99%

“…Elastic modulus is a measurand for the stiffness. It is the measure of the material's resistance to deformation in either compression or tension namely the elasticity modulus (E) and in shear namely the shear modulus (µ) [2]. Muscles and fibrous tissue are more resistant to deformation than other compliant tissues such as fat, due to their higher elastic moduli [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover; the shear modulus is related to the elasticity modulus by (5) [1,2]. (5) where υ is the Poisson's ratio.…”

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On Shear Wave Speed Estimation for Agar-Gelatine Phantom

Ahmed¹,

Salem²,

Seddik³

et al. 2016

ijacsa

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Abstract-Conventional imaging of diagnostic ultrasound is widely used. Although it makes the differences in the soft tissues echogenicities' apparent and clear, it fails in describing and estimating the soft tissue mechanical properties. It cannot portray their mechanical properties, such as the elasticity and stiffness. Estimating the mechanical properties increases chances of the identification of lesions or any pathological changes. Physicians are now characterizing the tissue's mechanical properties as diagnostic metrics. Estimating the tissue's mechanical properties is achieved by applying a force on the tissue and calculating the resulted shear wave speed. Due to the difficulty of calculating the shear wave speed precisely inside the tissue, it is estimated by analyzing ultrasound images of the tissue at a very high frame rate. In this paper, the shear wave speed is estimated using finite element analysis. A model is constructed to simulate the tissue's mechanical properties. For a generalized soft tissue model, Agar-gelatine model is used because it has properties similar to that of the soft tissue. A point force is applied at the center of the proposed model. As a result of this force, a deformation is caused. Peak displacements are tracked along the lateral dimension of the model for estimating the shear wave speed of the propagating wave using the Time-To-Peak displacement (TTP) method. Experimental results have shown that the estimated speed of the shear wave is 5.2 m/sec. The speed value is calculated according to shear wave speed equation equals about 5.7 m/sec; this means that our speed estimation system's accuracy is about 91 %, which is reasonable shear wave speed estimation accuracy with a less computational power compared to other tracking methods.

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Dynamic brain ADC variations over the cardiac cycle and their relation to tissue strain assessed with DENSE at high‐field MRI

Sloots

Froeling

Biessels

et al. 2022

Magnetic Resonance in Med

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The ADC of brain tissue slightly varies over the cardiac cycle. This variation could reflect physiology, including mixing of the interstitial fluid, relevant for brain waste clearance. However, it is known from cardiac diffusion imaging that tissue deformation by itself affects the magnitude of the MRI signal, leading to artificial ADC variations as well. This study investigates to what extent tissue deformation causes artificial ADC variations in the brain. Theory and Methods:We implemented a high-field MRI sequence with stimulated echo acquisition mode that simultaneously measures brain tissue deformation and ADC. Based on the measured tissue deformation, we simulated the artificial ADC variation by combining established theoretical frameworks and compared the results with the measured ADC variation. We acquired data in 8 healthy volunteers with diffusion weighting b = 300 and b = 1000 s/mm 2 .Results: Apparent diffusion coefficient variation was largest in the feet-to-head direction and showed the largest deviation from the mean ADC at peak systole. Artificial ADC variation estimated from tissue deformation was 1.3 ± 0.37⋅10 −5 mm 2 /s in the feet-to-head direction for gray matter, and 0.75 ± 0.29⋅10 −5 mm 2 /s for white matter. The measured ADC variation in the feet-to-head direction was 5.6⋅10 −5 ± 1.5⋅10 −5 mm 2 /s for gray matter and 3.2⋅10 −5 ± 1.0⋅10 −5 mm 2 /s for white matter, which was a factor of 3.5 ± 0.82 and 3.4 ± 0.57 larger than the artificial diffusion variations. The measured diffusion variations in the right-to-left/anterior-to-posterior direction were a factor of 1.5 ± 1.0/1.7 ± 1.4 and 2.0 ± 0.91/2.5 ± 0.94 larger than the artificial diffusion variations for gray matter and white matter, respectively. Conclusion: Apparent diffusion coefficient variations in the brain likely largely reflect physiology.

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Introduction

Cited by 68 publications

References 0 publications

Measurement of Deformation and Force for Microcantilevers Based on Feature Point Matching and Digital Image Correlation

Measurement of Deformation and Force for Microcantilevers Based on Feature Point Matching and Digital Image Correlation

On Shear Wave Speed Estimation for Agar-Gelatine Phantom

Dynamic brain ADC variations over the cardiac cycle and their relation to tissue strain assessed with DENSE at high‐field MRI

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