AI-driven projection tomography with multicore fibre-optic cell rotation.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
02 Jan 2024
02 Jan 2024
Historique:
received:
26
06
2023
accepted:
06
12
2023
medline:
4
1
2024
pubmed:
4
1
2024
entrez:
3
1
2024
Statut:
epublish
Résumé
Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.
Identifiants
pubmed: 38167247
doi: 10.1038/s41467-023-44280-1
pii: 10.1038/s41467-023-44280-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
147Subventions
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : CZ55/40-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : CZ55/40-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : CZ55/40-1
Informations de copyright
© 2024. The Author(s).
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