{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hu Z"],"funding":["European Research Council","Deutsche Forschungsgemeinschaft"],"pagination":["e11933"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12806437"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(3)"],"pubmed_abstract":["Understanding three Dimensional (3D) fundamental processes is crucial for academic and industrial applications. Nowadays, X-ray time-resolved tomography, or tomoscopy, is a leading technique for in situ and operando 4D (3D+time) characterization. Despite its ability to achieve 1000 tomograms per second at large-scale X-ray facilities, its applicability is limited by the centrifugal forces exerted on samples and the challenges of developing suitable environments for such high-speed studies. Here, Super Time-Resolved Tomography (STRT) is introduced, an approach that has the potential to enhance the temporal resolution of tomoscopy by at least an order of magnitude while preserving spatial resolution. STRT exploits a 4D Deep Learning (DL) reconstruction algorithm to produce high-fidelity 3D r"],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Super Time-Resolved Tomography."],"pmcid":["PMC12806437"],"funding_grant_id":["948426","408321454","101046448"],"pubmed_authors":["Josefsson K","Yao Z","Hu Z","Zhang Y","Garcia-Moreno F","Villanueva-Perez P","Makowska M"],"additional_accession":[]},"is_claimable":false,"name":"Super Time-Resolved Tomography.","description":"Understanding three Dimensional (3D) fundamental processes is crucial for academic and industrial applications. Nowadays, X-ray time-resolved tomography, or tomoscopy, is a leading technique for in situ and operando 4D (3D+time) characterization. Despite its ability to achieve 1000 tomograms per second at large-scale X-ray facilities, its applicability is limited by the centrifugal forces exerted on samples and the challenges of developing suitable environments for such high-speed studies. Here, Super Time-Resolved Tomography (STRT) is introduced, an approach that has the potential to enhance the temporal resolution of tomoscopy by at least an order of magnitude while preserving spatial resolution. STRT exploits a 4D Deep Learning (DL) reconstruction algorithm to produce high-fidelity 3D r","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-12T03:09:57.947Z","creation":"2026-06-12T03:07:47.225Z"},"accession":"S-EPMC12806437","cross_references":{"pubmed":["41164925"],"doi":["10.1002/advs.202511933"]}}