{"doi":"10.1364/oe.446241","title":"Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging","abstract":"Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky microscope objective inside the brain. By creating a self-consistent deep neural network that is trained to reconstruct anthropocentric images from the raw signal transported by the cannula, we demonstrate a single-cell resolution (< 10μm), depth sectioning resolution of 40 μm, and field of view of 200 μm, all with green-fluorescent-protein labelled neurons imaged at depths as large as 1.4 mm from the brain surface. Since ground-truth images at these depths are challenging to obtain in vivo, we propose a novel ensemble method that averages the reconstructed images from disparate deep-neural-network architectures. Finally, we demonstrate dynamic imaging of moving GCaMp-labelled C. elegans worms. Our approach dramatically simplifies deep-brain microscopy.","journal":"Optics Express","year":2021,"id":193566,"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":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9562,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":519771,"name":"Soren Nelson","orcid":null,"position":1,"is_corresponding":false},{"id":762039,"name":"Matthew Regier","orcid":"0000-0002-4641-3316","position":2,"is_corresponding":false},{"id":463419,"name":"M. Wayne Davis","orcid":"0009-0001-5222-3463","position":3,"is_corresponding":false},{"id":463421,"name":"Erik M. Jørgensen","orcid":"0000-0002-2978-8028","position":4,"is_corresponding":false},{"id":465468,"name":"Jason D. Shepherd","orcid":"0000-0001-7384-8289","position":5,"is_corresponding":false},{"id":518138,"name":"Rajesh Menon","orcid":"0000-0002-4777-376X","position":6,"is_corresponding":false},{"id":518137,"name":"Ruipeng Guo","orcid":"0000-0003-1963-0080","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T23:49:51.300073Z","pmid":"35209312","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":[]}