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Research Papers

Finite Element Based Optimization of Human Fingertip Optical Elastography

[+] Author and Article Information
Altaf A. Khan

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
842 W. Taylor Street MC 251,
Chicago, IL 60607-7052
e-mail: akhan49@uic.edu

Steven P. Kearney

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
842 W. Taylor Street MC 251,
Chicago, IL 60607-7052;
Advanced Photon Source,
Argonne National Laboratory,
9700 S Cass Avenue,
Argonne, IL 60439

Thomas J. Royston

Richard and Loan Hill Department of
Bioengineering,
University of Illinois at Chicago,
851 South Morgan Street MC 063,
Chicago, IL 60607-7072

Manuscript received January 26, 2018; final manuscript received May 3, 2018; published online June 5, 2018. Editor: Ahmed Al-Jumaily.

ASME J of Medical Diagnostics 1(3), 031007 (Jun 05, 2018) (8 pages) Paper No: JESMDT-18-1003; doi: 10.1115/1.4040199 History: Received January 26, 2018; Revised May 03, 2018

Dynamic elastography methods attempt to quantitatively map soft tissue viscoelastic properties. Application to the fingertip, relevant to medical diagnostics and to improving tactile interfaces, is a novel and challenging application, given the small target size. In this feasibility study, an annular actuator placed on the surface of the fingertip and driven harmonically at multiple frequencies sequentially creates geometrically focused surface (GFS) waves. These surface wave propagation patterns are measured using scanning laser Doppler vibrometry. Reconstruction (the inverse problem) is performed in order to estimate fingertip soft tissue viscoelastic properties. The study identifies limitations of an analytical approach and introduces an optimization approach that utilizes a finite element (FE) model. Measurement at multiple frequencies reveals limitations of an assumption of homogeneity of material properties. Identified shear viscoelastic properties increase significantly as frequency increases and the depth of penetration of the surface wave is reduced, indicating that the fingertip is significantly stiffer near its surface.

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References

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Figures

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Fig. 1

Shear viscoelastic models used for study

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Fig. 2

Titanium alloy actuator (left) with arrows showing direction of motion and line of sight required by SLDV (right) showing finger in actuator

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Fig. 3

Finite element solid model of finger. Blue (dark gray in grayscale) circle denotes actuation.

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Fig. 4

Wave propagation at 1 kHz for (left) ring actuator on surface and (right) cylindrical wall actuation

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Fig. 5

Identifying shear storage and loss moduli using analytical approximation: (left) ring actuator on surface; (right) cylindrical wall actuation. Key: actual storage (blue) and loss (green) moduli; X and O best fits to storage and loss moduli, respectively, using analytical approximation. Resulting best fit rheological (springpot) model based on X and O best fits (color online).

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Fig. 6

FEA optimization fit at 1 kHz

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Fig. 7

Best fits of different viscoelastic models to FE-based estimate of storage and loss moduli based on experimental measurements: (left) storage and (right) loss moduli

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Fig. 8

Identifying shear storage and loss moduli using FE-based optimization method: (left) ring actuator on surface; (right) cylindrical wall actuation. Key: actual storage (blue) and loss (green) moduli (shear storage modulus is large than loss modulus); X and O best fits to storage and loss moduli, respectively, using FE-based optimization method. Resulting best fit rheological (springpot) model based on X and O best fits (exact match and overlays the actual values) (color online).

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