Dr. Gavin Docherty Profile

Dr. Gavin Docherty

at Univ of British Columbia

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Proceedings Article | 4 March 2019 Presentation

Multi-acquisition averaging OCT-A for diabetic retinopathy (Conference Presentation)

Arman Athwal, Morgan Heisler, Myeong Jin Ju, Rosanna Martens, Mahadev Bhalla, Gavin Docherty, Zaid Mammo, Yifan Jian, Mirza Faisal Beg, Eduardo Navajas, Marinko Sarunic

Proceedings Volume 10867, 108671C (2019) https://doi.org/10.1117/12.2510997

KEYWORDS: Image registration, Image segmentation, Visualization, Image quality, Vascular diseases, Optical coherence tomography, Angiography, Image processing

Read Abstract +

High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, especially Diabetic Retinopathy (DR), which is a major cause of visual morbidity and is 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 individuals with DR whose ability to fixate is limited due to deteriorating vision. The sequential acquisition and averaging of multiple OCT-A images can be performed for removing motion artifact and increasing the contrast of the vascular network. As motion artifacts often irreversibly corrupt OCT-A images of DR eyes, a robust registration pipeline is needed before feature preserving image averaging can be performed. In this report we present an improvement upon a novel method for the acquisition, processing, segmentation, registration, and averaging of sequentially acquired OCT-A images, to correct for motion artifacts in images of DR eyes. Image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration and subsequent local similarity-based non-rigid registration. Where our previous work was limited by the need for at least one image containing no motion artifact, thus reducing its clinical relevance, this novel template-less method stitches together partial images to form complete, motion-free images. These techniques significantly improve image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.

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