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Proceedings Paper

GPU accelerated optical coherence tomography angiography using strip-based registration (Conference Presentation)
Author(s): Morgan Heisler; Sieun Lee; Zaid Mammo; Yifan Jian; Myeong Jin Ju; Dongkai Miao; Eric Raposo; Daniel J. Wahl; Andrew Merkur; Eduardo Navajas; Chandrakumar Balaratnasingam; Mirza Faisal Beg; Marinko V. Sarunic
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Paper Abstract

High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, which are a major cause of visual morbidity and are increasing in prevalence. Optical Coherence Tomography Angiography (OCT-A) images are acquired over multiple seconds and are particularly susceptible to motion artifacts, which are more prevalent when imaging patients with pathology whose ability to fixate is limited. The acquisition of multiple OCT-A images sequentially can be performed for the purpose of removing motion artifact and increasing the contrast of the vascular network through averaging. Due to the motion artifacts, a robust registration pipeline is needed before feature preserving image averaging can be performed. In this report, we present a novel method for a GPU-accelerated pipeline for acquisition, processing, segmentation, and registration of multiple, sequentially acquired OCT-A images to correct for the motion artifacts in individual images for the purpose of averaging. High performance computing, blending CPU and GPU, was introduced to accelerate processing in order to provide high quality visualization of the retinal microvasculature and to enable a more accurate quantitative analysis in a clinically useful time frame. Specifically, image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration estimated using Scale Invariant Feature Transform (SIFT) keypoints and subsequent local similarity-based non-rigid registration. These techniques improve the image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.

Paper Details

Date Published: 19 April 2017
PDF: 1 pages
Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 1005306 (19 April 2017); doi: 10.1117/12.2253588
Show Author Affiliations
Morgan Heisler, Simon Fraser Univ. (Canada)
Sieun Lee, Simon Fraser Univ. (Canada)
Zaid Mammo, The Univ. of British Columbia (Canada)
Yifan Jian, Simon Fraser Univ. (Canada)
Myeong Jin Ju, Simon Fraser Univ. (Canada)
Dongkai Miao, Simon Fraser Univ. (Canada)
Eric Raposo, Simon Fraser Univ. (Canada)
Daniel J. Wahl, Simon Fraser Univ. (Canada)
Andrew Merkur, The Univ. of British Columbia (Canada)
Eduardo Navajas, The Univ. of British Columbia (Canada)
Chandrakumar Balaratnasingam, The Univ. of British Columbia (Canada)
The Univ. of Western Australia (Australia)
Vitreous Retina Macula Consultants of New York (United States)
Mirza Faisal Beg, Simon Fraser Univ. (Canada)
Marinko V. Sarunic, Simon Fraser Univ. (Canada)


Published in SPIE Proceedings Vol. 10053:
Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI
James G. Fujimoto; Joseph A. Izatt; Valery V. Tuchin, Editor(s)

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