{"doi":"10.1364/boe.541685","title":"Predictive coding compressive sensing optical coherence tomography hardware implementation","abstract":"Compressed sensing (CS) is an approach that enables comprehensive imaging by reducing both imaging time and data density, and is a theory that enables undersampling far below the Nyquist sampling rate and guarantees high-accuracy image recovery. Prior efforts in the literature have focused on demonstrations of synthetic undersampling and reconstructions enabled by compressed sensing. In this paper, we demonstrate the first physical, hardware-based sub-Nyquist sampling with a galvanometer-based OCT system with subsequent reconstruction enabled by compressed sensing. Acquired images of a variety of samples, with volume scanning time reduced by 89% (12.5% compression rate), were successfully reconstructed with relative error (RE) of less than 20% and mean square error (MSE) of around 1%.","journal":"Biomedical Optics Express","year":2024,"id":481922,"datarank":0.12080565146901906,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.016833574385027243,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.016833574385027243,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.945,"is_data_producer":true,"deposit_databanks":{"figshare":["10.6084/m9.figshare.27247101"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":773162,"name":"Haiqiu Yang","orcid":null,"position":1,"is_corresponding":false},{"id":1322275,"name":"Zizheng Jia","orcid":null,"position":2,"is_corresponding":false},{"id":1321840,"name":"Arielle S. Joasil","orcid":"0009-0003-8663-4679","position":3,"is_corresponding":false},{"id":1321841,"name":"Xinran Gao","orcid":"0009-0007-5706-985X","position":4,"is_corresponding":false},{"id":728352,"name":"Christine P. Hendon","orcid":"0000-0001-7318-1517","position":5,"is_corresponding":false},{"id":1124617,"name":"Diego M. Song Cho","orcid":"0009-0007-7492-0933","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T02:07:10.067421Z","pmid":"39553866","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":[]}