Flow holographic tomography is emerging as a powerful tool for label-free three-dimensional imaging of biological specimens in dynamic environments. By combining digital holography, quantitative phase imaging, and tomographic reconstruction methods, this technique enables the retrieval of the volumetric refractive index distribution of cells while they are freely flowing in microfluidic systems. Such an approach provides quantitative and non-invasive access to cellular morphology and biophysical properties without the need for fluorescent markers or staining procedures. This talk will present recent progress in holographic tomography for flowing cells, with particular emphasis on computational reconstruction strategies, flow-driven acquisition geometries, and high-throughput implementations. Special attention will be devoted to the growing role of artificial intelligence and deep learning methods in holographic tomography, including AI-assisted phase recovery, tomographic reconstruction, extraction and analysis of subcellular features. The integration of data-driven approaches with holographic imaging is opening new perspectives for faster, more robust, and scalable 3D quantitative imaging in flow conditions.The talk will also illustrate also the use of virtual reality environments for the immersive visualization and analysis of reconstructed cellular volumes, enabling enhanced inspection of three-dimensional cellular features and morphological details. Applications ranging from biomedical diagnostics to cell phenotyping and liquid biopsy will be highlighted, showing how flow holographic tomography is evolving toward intelligent and interactive platforms for real-time, label-free 3D bioimaging of complex cellular systems.
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