March 2012
Volume 53, Issue 14
Free
ARVO Annual Meeting Abstract  |   March 2012
Characterizing the Tissue Engineered Cornea Stroma by CUDA GPU Accelerated Computing
Author Affiliations & Notes
  • Yonggang Pang
    Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois
  • Xiaoli Wang
    Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois
  • Mingqiao Peng
    Northwest China Grid Company, Xi'an, China
  • Ping Bu
    Ophthalmology, Loyola University Medical Center, Maywood, Illinois
  • Charles S. Bouchard
    Ophthalmology, Loyola University Medical Center, Maywood, Illinois
  • Footnotes
    Commercial Relationships  Yonggang Pang, None; Xiaoli Wang, None; Mingqiao Peng, None; Ping Bu, None; Charles S. Bouchard, None
  • Footnotes
    Support  Illinois Society for the Prevention of Blindness, NVIDIA Academic Partnership
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 338. doi:https://doi.org/
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      Yonggang Pang, Xiaoli Wang, Mingqiao Peng, Ping Bu, Charles S. Bouchard; Characterizing the Tissue Engineered Cornea Stroma by CUDA GPU Accelerated Computing. Invest. Ophthalmol. Vis. Sci. 2012;53(14):338. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose:
 

The unique aligned structure of collagen in corneal stroma plays a fundamental role in the optical properties of the cornea. A tissue engineered cornea using collagen hydrogel would also need an aligned architecture in order to generate a promising alternative. Effective characterization techniques are required in order to control the structures of an engineered cornea to resemble that of the normal human cornea. In the current project, we used a precisely controlled stretching technique to generate collagen hydrogel with an aligned structure and used our in-house developed CUDA GPU (graphic processing unit) accelerated image processing technique to characterize the dynamics of the collagen fiber alignment.

 
Methods:
 

Bovine type I collagen hydrogel was polymerized and placed in a custom made stretching device mounted on a confocal microscope stage. After polymerization, the collagen hydrogel was stretched at 1% length incensement by a micromanipulator. At each incensement, the micro architecture of the collagen hydrogel was imaged by reflection confocal microscopy in the stack scanning mode. The acquired images were recorded onto one computer and streamed simultaneously to another computer equipped with a multiple-core NVIDIA GPU. A Fast Fourier Transform(FFT) algorithm was used to analyze the degree of collagen alignment. Computer programs were developed using Matlab and CUDA, which is the parallel computing platform and programming model from NVIDIA, for High speed image processing.

 
Results:
 

The micro architecture of the collagen hydrogel was successfully imaged by reflection confocal microscopy and it changed from a random to an aligned pattern after stretching. The degree of alignment increased as stretching reached higher degrees. Recording the collagen hydrogel dynamics generated large amount of images, which were up to tens of Gigabytes in size. Conventional CPU (central processing unit) based image processing techniques were not capable of finishing the image processing in required time period. CUDA GPU accelerated image processing increased the alignment characterization speed by 50 times compared with CPU based image processing, which brought the alignment analysis to real-time level.

 
Conclusions:
 

Controlled mechanical stretching showed promising results for engineering cornea stroma. Reflection confocal microscopy and CUDA GPU image processing are powerful tools to characterize the dynamics of collagen alignment.

 
Keywords: cornea: stroma and keratocytes • microscopy: confocal/tunneling • image processing 
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