{"doi":"10.1364/boe.396771","title":"High compression deep learning based single-pixel hyperspectral macroscopic fluorescence lifetime imaging in vivo","abstract":"Single pixel imaging frameworks facilitate the acquisition of high-dimensional optical data in biological applications with photon starved conditions. However, they are still limited to slow acquisition times and low pixel resolution. Herein, we propose a convolutional neural network for fluorescence lifetime imaging with compressed sensing at high compression (NetFLICS-CR), which enables in vivo applications at enhanced resolution, acquisition and processing speeds, without the need for experimental training datasets. NetFLICS-CR produces intensity and lifetime reconstructions at 128 × 128 pixel resolution over 16 spectral channels while using only up to 1% of the required measurements, therefore reducing acquisition times from ∼2.5 hours at 50% compression to ∼3 minutes at 99% compression. Its potential is demonstrated in silico, in vitro and for mice in vivo through the monitoring of receptor-ligand interactions in liver and bladder and further imaging of intracellular delivery of the clinical drug Trastuzumab to HER2-positive breast tumor xenografts. The data acquisition time and resolution improvement through NetFLICS-CR, facilitate the translation of single pixel macroscopic flurorescence lifetime imaging (SP-MFLI) for in vivo monitoring of lifetime properties and drug uptake.","journal":"Biomedical Optics Express","year":2020,"id":97220,"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":33,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9469,"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":481058,"name":"Alena Rudkouskaya","orcid":"0000-0002-3632-9743","position":1,"is_corresponding":false},{"id":481059,"name":"Rui Yao","orcid":"0000-0002-3415-9859","position":2,"is_corresponding":false},{"id":291108,"name":"Pingkun Yan","orcid":"0000-0002-9779-2141","position":3,"is_corresponding":false},{"id":473911,"name":"Margarida Barroso","orcid":"0000-0002-0407-3181","position":4,"is_corresponding":false},{"id":287746,"name":"Xavier Intes","orcid":"0000-0001-5868-4845","position":5,"is_corresponding":false},{"id":339135,"name":"Marien Ochoa","orcid":"0000-0001-6427-4447","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-18T22:35:34.520494Z","pmid":"33149959","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":[]}