GPU accelerated optical coherence tomography angiography using strip-based registration (Conference Presentation)

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

doi:10.1117/12.2253588

19 April 2017 GPU accelerated optical coherence tomography angiography using strip-based registration (Conference Presentation)

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

Author Affiliations +

Proceedings Volume 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI; 1005306 (2017) https://doi.org/10.1117/12.2253588
Event: SPIE BiOS, 2017, San Francisco, California, United States

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.

Conference Presentation

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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, and Marinko V. Sarunic "GPU accelerated optical coherence tomography angiography using strip-based registration (Conference Presentation)", Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 1005306 (19 April 2017); https://doi.org/10.1117/12.2253588

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KEYWORDS

Image registration

Optical coherence tomography

Angiography

Image segmentation

Visualization

Image quality

Image processing

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