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OpenCL framework for fast estimation of optical properties from spatial frequency domain images
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Paper Abstract

To overcome the drawbacks of the commonly used lookup table inverse models, we propose a novel custom OpenCL™- accelerated artificial neural network inverse model for spatial frequency domain imaging (https://bitbucket.org/xopto /rftroop). Utilizing a mid-range graphics processing unit, the proposed inverse model can estimate high-definition (1920 × 1080) maps of the absorption and reduced scattering coefficients and two scattering phase function related quantifiers at a rate of more than 50 frames per second. We show that the artificial neural network inverse model can be seamlessly extended to estimate multiple optical properties independently, thus providing a versatile framework that allows introduction of new quantifiers.

Paper Details

Date Published: 4 March 2019
PDF: 8 pages
Proc. SPIE 10889, High-Speed Biomedical Imaging and Spectroscopy IV, 1088919 (4 March 2019); doi: 10.1117/12.2509986
Show Author Affiliations
Peter Naglič, Univ. of Ljubljana (Slovenia)
Yevhen Zelinskyi, Univ. of Ljubljana (Slovenia)
Boštjan Likar, Univ. of Ljubljana (Slovenia)
Franjo Pernuš, Univ. of Ljubljana (Slovenia)
Miran Bürmen, Univ. of Ljubljana (Slovenia)


Published in SPIE Proceedings Vol. 10889:
High-Speed Biomedical Imaging and Spectroscopy IV
Kevin K. Tsia; Keisuke Goda, Editor(s)

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