{"doi":"10.1007/3-540-39999-2_56","title":"A Distributed Rendering System “On Demand Rendering System”","abstract":null,"journal":"Lecture Notes in Computer Science","year":2000,"id":675991,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1766295,"name":"Toshihiko Kobayashi","orcid":null,"position":1,"is_corresponding":false},{"id":1766296,"name":"Yasuhiro Takeda","orcid":null,"position":2,"is_corresponding":false},{"id":1766297,"name":"Hiroshi Hoshino","orcid":null,"position":3,"is_corresponding":false},{"id":1766298,"name":"Xiuyi Jin","orcid":null,"position":4,"is_corresponding":false},{"id":1766294,"name":"Hideo Miyachi","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Distributed Rendering System “On Demand Rendering System”","abstract":"UNLABELLED: Fluorescence lifetime imaging microscopy (FLIM) is a powerful quantitative technique that provides metabolic and molecular contrast, offering strong translational potential for label-free, real-time diagnostics. However, its clinical adoption remains limited by long pixel dwell times and low signal-to-noise ratio (SNR), which impose a more severe resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIMPSR_k, a deep learning-based multi-channel pixel super-resolution (PSR) framework that reconstructs high-resolution FLIM images from data acquired with up to a fivefold increased pixel size. The model is trained using the conditional generative adversarial network (cGAN) framework, which, compared to diffusion model-based alternatives, delivers a more robust PSR reconstruction with substantially shorter inference times, a crucial advantage for practical deployment. FLIMPSR_k not only enables faster image acquisition but can also alleviate SNR limitations in autofluorescence-based FLIM. Blind testing on held-out patient-derived tumor tissue samples demonstrates that FLIMPSR_k reliably achieves a super-resolution factor of k = 5, resulting in a 25-fold increase in the space-bandwidth product of the output images and revealing fine architectural features lost in lower-resolution inputs, with statistically significant improvements across various image quality metrics. By increasing FLIM's effective spatial resolution, FLIMPSR_k advances lifetime imaging toward faster, higher-resolution, and hardware-flexible implementations compatible with low-numerical-aperture and miniaturized platforms, better positioning FLIM for translational applications.\nSUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43074-026-00277-9.","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"42603887","pmcid":null,"openalex_id":"https://openalex.org/W1552410400","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.05544933,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://link.springer.com/content/pdf/10.1007/3-540-39999-2_56","host_type":"publisher"},{"url":"https://doi.org/10.1007/3-540-39999-2_56","host_type":"book series"}],"fields_of_study":["Data Visualization and Analytics","Computer Graphics and Visualization Techniques"],"mesh_terms":[],"keywords":["Rendering (computer graphics)","Computer science","Parallel rendering","Software rendering","Tiled rendering","Visualization","Real-time rendering","Alternate frame rendering","3D rendering","Computer graphics (images)","Distributed computing","Computer graphics","Artificial intelligence","3D computer graphics","Computational microscopy","Conditional generative adversarial networks (cGAN)","Deep learning","Fluorescence lifetime imaging microscopy (FLIM)","Pixel super-resolution"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T01:56:55.098042Z","pmid":null,"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":[]}