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Technical Brief

Appropriate Objective Functions for Quantifying Iris Mechanical Properties Using Inverse Finite Element Modeling

[+] Author and Article Information
Anup D. Pant

Department of Biomedical Engineering,
The University of Akron,
Akron, OH 44325
e-mail: adp63@zips.uakron.edu

Syril K. Dorairaj

Department of Ophthalmology,
Mayo Clinic,
Jacksonville, FL 32224
e-mail: dorairaj.syril@mayo.edu

Rouzbeh Amini

Mem. ASME
Department of Biomedical Engineering,
The University of Akron,
Akron, OH 44325
e-mail: ramini@uakron.edu

1Corresponding author.

Manuscript received July 7, 2017; final manuscript received February 27, 2018; published online April 30, 2018. Assoc. Editor: Thao (Vicky) Nguyen.

J Biomech Eng 140(7), 074502 (Apr 30, 2018) (6 pages) Paper No: BIO-17-1298; doi: 10.1115/1.4039679 History: Received July 07, 2017; Revised February 27, 2018

Quantifying the mechanical properties of the iris is important, as it provides insight into the pathophysiology of glaucoma. Recent ex vivo studies have shown that the mechanical properties of the iris are different in glaucomatous eyes as compared to normal ones. Notwithstanding the importance of the ex vivo studies, such measurements are severely limited for diagnosis and preclude development of treatment strategies. With the advent of detailed imaging modalities, it is possible to determine the in vivo mechanical properties using inverse finite element (FE) modeling. An inverse modeling approach requires an appropriate objective function for reliable estimation of parameters. In the case of the iris, numerous measurements such as iris chord length (CL) and iris concavity (CV) are made routinely in clinical practice. In this study, we have evaluated five different objective functions chosen based on the iris biometrics (in the presence and absence of clinical measurement errors) to determine the appropriate criterion for inverse modeling. Our results showed that in the absence of experimental measurement error, a combination of iris CL and CV can be used as the objective function. However, with the addition of measurement errors, the objective functions that employ a large number of local displacement values provide more reliable outcomes.

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Figures

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Fig. 1

Anteior chamber angle and iris configuration of (a) normal and (b) primary angle closure glaucoma eye (image modified from National Eye institute, NIH, Bethesda, MD). Scale bar is an approximation in the image.

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Fig. 2

Anterior segment optical coherence tomography of a normal eye. Iris CV is defined as the largest distance between the posterior surface of the iris and the iris CL. AS-OCT image is from Dr. Vanita Pathak-Ray, FRCS (Ed), FRCOphth (Lon), Consultant Glaucoma Specialist at VST Center for Glaucoma, LV Prasad Eye Institute, Hyderabad, India with permission.

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Fig. 3

Undeformed porcine iris model showing the axis of symmetry

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Fig. 4

Iris configuration (a) before and (b) after dilation showing changes in the CL and CV

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Fig. 5

(a) Undeformed porcine iris model and (b) deformed porcine iris model. Unique features such as grooves and bumps identified in both configuration are marked by alphabets A to J.

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Fig. 6

Inverse algorithm flowchart

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Fig. 7

Error contour plot shows the uniqueness of the solution for a particular G and ν for noise free nodal displacement based objective function

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