{"doi":"10.1109/tmi.2020.3005067","title":"Throughput-Speed Product Augmentation for Scanning Fiber-Optic Two-Photon Endomicroscopy","abstract":"Compactness, among several others, is one unique and very attractive feature of a scanning fiber-optic two-photon endomicroscope. To increase the scanning area and the total number of resolvable pixels (i.e., the imaging throughput), it typically requires a longer cantilever which, however, leads to a much undesired, reduced scanning speed (and thus imaging frame rate). Herein we introduce a new design strategy for a fiber-optic scanning endomicroscope, where the overall numerical aperture (NA) or beam focusing power is distributed over two stages: 1) a mode-field focuser engineered at the tip of a double-clad fiber (DCF) cantilever to pre-amplify the single-mode core NA, and 2) a micro objective of a lower magnification (i.e., ∼ 2× in this design) to achieve final tight beam focusing. This new design enables either an ~9-fold increase in imaging area (throughput) or an ~3-fold improvement in imaging frame rate when compared to traditional fiber-optic endomicroscope designs. The performance of an as-designed endomicroscope of an enhanced throughput-speed product was demonstrated by two representative applications: (1) high-resolution imaging of an internal organ (i.e., mouse kidney) in vivo over a large field of view without using any fluorescent contrast agents, and (2) real-time neural imaging by visualizing dendritic calcium dynamics in vivo with sub-second temporal resolution in GCaMP6m-expressing mouse brain. This cascaded NA amplification strategy is universal and can be readily adapted to other types of fiber-optic scanners in compact linear or nonlinear endomicroscopes.","journal":"IEEE Transactions on Medical Imaging","year":2020,"id":97863,"datarank":0.5244761342199721,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"self_citation_contribution":0.5244761342199721,"citation_network_contribution":0.0,"self_endowment_contribution":0.5244761342199721,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":394110,"name":"Hyeon‐Cheol Park","orcid":null,"position":1,"is_corresponding":false},{"id":483434,"name":"Kaiyan Li","orcid":"0000-0003-2275-5984","position":2,"is_corresponding":false},{"id":392892,"name":"Ang Li","orcid":"0000-0001-7461-9437","position":3,"is_corresponding":false},{"id":295608,"name":"Defu Chen","orcid":"0000-0002-7198-1104","position":4,"is_corresponding":false},{"id":392891,"name":"Honghua Guan","orcid":"0000-0002-4824-9185","position":5,"is_corresponding":false},{"id":392893,"name":"Yuanlei Yue","orcid":"0000-0002-0552-2911","position":6,"is_corresponding":false},{"id":484084,"name":"Yung‐Tian A. Gau","orcid":null,"position":7,"is_corresponding":false},{"id":17111,"name":"Dwight E. Bergles","orcid":"0000-0002-7133-7378","position":8,"is_corresponding":false},{"id":483435,"name":"Ming-Jun Li","orcid":"0000-0003-4141-6670","position":9,"is_corresponding":false},{"id":392895,"name":"Hui Lü","orcid":"0000-0003-2336-2170","position":10,"is_corresponding":false},{"id":295612,"name":"Xingde Li","orcid":"0000-0002-4725-3297","position":11,"is_corresponding":false},{"id":483433,"name":"Wenxuan Liang","orcid":"0000-0001-9143-7256","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T22:36:02.818637Z","pmid":"32746124","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":[]}