{"doi":"10.1364/oe.546788","title":"Artificial intelligence-assisted projection-resolved optical coherence tomographic angiography (aiPR-OCTA)","abstract":"We improved voxel-wise projection-resolved optical coherence tomographic angiography (PR-OCTA) using artificial intelligence. For generating a high-quality ground truth, our approach involved graders editing the flow signal to achieve an optimal appearance in the inner/outer retina and choroid through a rule-based PR-OCTA algorithm, ensuring the preservation of in situ flow signals (ground truth) while removing residual artifacts. The developed model employs a convolutional neural network to generate projection-resolved OCTA volumes from structural OCT and OCTA inputs. We evaluated the artificial intelligence PR-OCTA (aiPR-OCTA) algorithm on 126 normal eyes by assessing structural similarity (SSIM), flow signal-to-noise ratio (fSNR), and residual artifact strength. Compared to the existing state-of-the-art rule-based PR-OCTA algorithm, aiPR-OCTA demonstrated superior artifact removal, better preservation of flow signals, and accurate maintenance of anatomical details at the capillary scale. Additionally, it achieved a higher fSNR and reduced background artifacts.","journal":"Optics Express","year":2025,"id":530568,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9466,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":288601,"name":"Tristan T. Hormel","orcid":"0000-0002-7242-1934","position":1,"is_corresponding":false},{"id":295377,"name":"Steven T. Bailey","orcid":"0000-0003-4949-1464","position":2,"is_corresponding":false},{"id":288603,"name":"Thomas S. Hwang","orcid":"0000-0002-0535-4823","position":3,"is_corresponding":false},{"id":288604,"name":"Yali Jia","orcid":"0000-0002-2784-1905","position":4,"is_corresponding":false},{"id":288602,"name":"Jie Wang","orcid":"0000-0003-3417-3703","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T02:51:05.836974Z","pmid":"40797921","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}