Optical coherence tomography angiography (OCTA) has become an essential tool in clinics for structural and functional microvasculature imaging. However, a primary setback for OCTA is its imaging speed. The current protocols require high sampling density from raster scanning and multiple cross-sectional B-scan acquisitions to form a single image frame, limiting the acquisition speed. Although advanced ultrafast imaging systems have been proposed, extensive hardware adjustments are cost-prohibitive and pose limitations for practical implementations. Herein, we present an integrated deep learning (DL) method to simultaneously tackle the sampling density and the B-scan repetition process, thus improving the imaging speed while preserving quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully sampled, high quality (8 repeated B-scans) angiograms from their corresponding undersampled, low quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. We evaluate our proposed framework using an in-vivo mouse brain vasculature dataset and demonstrate that our method can enhance the OCTA acquisition speed while achieving superior reconstruction performance than conventional methods. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256× while preserving the image quality and thus provides a convenient software-only solution to aid preclinical and clinical studies.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.