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

Author

Listed:
  • Jiawei Sun

    (Shanghai Artificial Intelligence Laboratory
    TU Dresden
    TU Dresden)

  • Bin Yang

    (TU Dresden)

  • Nektarios Koukourakis

    (TU Dresden
    TU Dresden)

  • Jochen Guck

    (Max Planck Institute for the Science of Light & Max Planck-Zentrum für Physik und Medizin)

  • Juergen W. Czarske

    (TU Dresden
    TU Dresden
    TU Dresden
    TU Dresden)

Abstract

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.

Suggested Citation

  • Jiawei Sun & Bin Yang & Nektarios Koukourakis & Jochen Guck & Juergen W. Czarske, 2024. "AI-driven projection tomography with multicore fibre-optic cell rotation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44280-1
    DOI: 10.1038/s41467-023-44280-1
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    References listed on IDEAS

    as
    1. Kyoohyun Kim & YongKeun Park, 2017. "Tomographic active optical trapping of arbitrarily shaped objects by exploiting 3D refractive index maps," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    2. Steven Peiran Zhang & James Lata & Chuyi Chen & John Mai & Feng Guo & Zhenhua Tian & Liqiang Ren & Zhangming Mao & Po-Hsun Huang & Peng Li & Shujie Yang & Tony Jun Huang, 2018. "Digital acoustofluidics enables contactless and programmable liquid handling," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
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