日本語
 
Help Privacy Policy ポリシー/免責事項
  詳細検索ブラウズ

アイテム詳細


公開

学術論文

AI-driven projection tomography with multicore fibre-optic cell rotation

MPS-Authors
/persons/resource/persons241284

Guck,  Jochen
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;

External Resource
There are no locators available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
フルテキスト (公開)

Nat Commun 2023 Sun.pdf
(出版社版), 3MB

付随資料 (公開)
There is no public supplementary material available
引用

Sun, J., Yang, B., Koukourakis, N., Guck, J., & Czarske, J. W. (2024). AI-driven projection tomography with multicore fibre-optic cell rotation. Nature Communications, 15:. doi:10.1038/s41467-023-44280-1.


引用: https://hdl.handle.net/21.11116/0000-000E-5BAD-A
要旨
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.