AI-driven projection tomography with multicore fibre-optic cell rotation
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 limi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-44280-1 |
| _version_ | 1797363033635291136 |
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| author | Jiawei Sun Bin Yang Nektarios Koukourakis Jochen Guck Juergen W. Czarske |
| author_facet | Jiawei Sun Bin Yang Nektarios Koukourakis Jochen Guck Juergen W. Czarske |
| author_sort | Jiawei Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:15:56Z |
| format | Article |
| id | doaj.art-1807668e2e804805b52bf61a55aed2f2 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-08T16:15:56Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-1807668e2e804805b52bf61a55aed2f22024-01-07T12:33:54ZengNature PortfolioNature Communications2041-17232024-01-0115111110.1038/s41467-023-44280-1AI-driven projection tomography with multicore fibre-optic cell rotationJiawei Sun0Bin Yang1Nektarios Koukourakis2Jochen Guck3Juergen W. Czarske4Shanghai Artificial Intelligence LaboratoryLaboratory of Measurement and Sensor System Technique (MST), TU DresdenCompetence Center for Biomedical Computational Laser Systems (BIOLAS), TU DresdenMax Planck Institute for the Science of Light & Max Planck-Zentrum für Physik und MedizinCompetence Center for Biomedical Computational Laser Systems (BIOLAS), TU DresdenAbstract 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.https://doi.org/10.1038/s41467-023-44280-1 |
| spellingShingle | Jiawei Sun Bin Yang Nektarios Koukourakis Jochen Guck Juergen W. Czarske AI-driven projection tomography with multicore fibre-optic cell rotation Nature Communications |
| title | AI-driven projection tomography with multicore fibre-optic cell rotation |
| title_full | AI-driven projection tomography with multicore fibre-optic cell rotation |
| title_fullStr | AI-driven projection tomography with multicore fibre-optic cell rotation |
| title_full_unstemmed | AI-driven projection tomography with multicore fibre-optic cell rotation |
| title_short | AI-driven projection tomography with multicore fibre-optic cell rotation |
| title_sort | ai driven projection tomography with multicore fibre optic cell rotation |
| url | https://doi.org/10.1038/s41467-023-44280-1 |
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