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Opt. Express"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>\n                    Eye movements, optical opacities, and other factors can introduce artifacts during the acquisition of optical coherence tomography angiography volumes, resulting in suboptimal imaging quality. We aim to develop an automated deep learning model to separate excellent-quality from suboptimal-quality volumes in a quantitative and objective manner. Existing works use supervised classifiers trained on 2D\n                    <jats:italic>en face<\/jats:italic>\n                    images, which 1) represent quality as rigid and discrete classes, 2) require large amounts of labeled data for every type of artifact to generalize effectively, and 3) discard valuable depth information from the original volume. We propose OCTA-GAN, an efficient 3D generative adversarial network architecture that incorporates multi-scale processing layers to assess the quality of scans by fusing fine vasculature details with larger anatomical context. The unsupervised model learns patterns associated with excellent-quality volumes and accurately determines the quality of unseen volumes. Experimental results show OCTA-GAN\u2019s discriminator distinguishes excellent-quality from suboptimal-quality volumes with an AUC of 0.92, a sensitivity of 95.7%, and a specificity of 76.6%, surpassing the baseline 3D architecture (AUC\u2009=\u20090.55, sensitivity\u2009=\u200997.8%, specificity\u2009=\u200912.8%). Further analysis attributes the improved performance to the synergy between the generator model and discriminator architecture, whose robust feature representations effectively capture the intricate vasculature. Comparison with state-of-the-art 2D supervised\n                    <jats:italic>en face<\/jats:italic>\n                    classifiers demonstrates OCTA-GAN\u2019s ability to generalize across diverse artifacts and provides an interpretable organization of the output scores based on severity.\n                  <\/jats:p>","DOI":"10.1364\/boe.573843","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T16:00:07Z","timestamp":1763481607000},"page":"378","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised quality assessment with generative adversarial networks for 3D OCTA microvascular imaging"],"prefix":"10.1364","volume":"17","author":[{"given":"Edmund","family":"Sumpena","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00za53h95","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Johns Hopkins University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4597-2682","authenticated-orcid":true,"given":"Andrew","family":"Cornelio","sequence":"additional","affiliation":[{"name":"Johns Hopkins University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Collazo","sequence":"additional","affiliation":[{"name":"Johns Hopkins University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu Jie","family":"Ting","sequence":"additional","affiliation":[{"name":"The Framingham Heart Study"},{"name":"Boston University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Kowalczyk","sequence":"additional","affiliation":[{"name":"The Framingham Heart Study"},{"name":"Boston University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Southern California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexa","family":"Beiser","sequence":"additional","affiliation":[{"name":"The Framingham Heart Study"},{"name":"Boston University School of Medicine"},{"name":"Boston University School of Public Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudha","family":"Seshadri","sequence":"additional","affiliation":[{"name":"The Framingham Heart Study"},{"name":"Boston University School of Medicine"},{"name":"Glenn Biggs Institute for Alzheimer\u2019s & Neurodegenerative Diseases"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9767-7920","authenticated-orcid":true,"given":"Amir H.","family":"Kashani","sequence":"additional","affiliation":[{"name":"Johns Hopkins University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0629-3006","authenticated-orcid":true,"given":"Craig K.","family":"Jones","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00za53h95","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Johns Hopkins University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"boe-17-1-378-R1","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.preteyeres.2017.07.002","type":"journal-article","volume":"60","author":"Kashani","year":"2017","journal-title":"Prog. 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