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Imaging and analysis of many single cells hold great potential in ourunderstanding of heterogeneous and complex life systems and in enablingbiomedical applications. We here introduce a recently realized image-free"imaging" cytometry technology, which we call ghost cytometry. While acompressive ghost imaging technique utilizing object's motion relative to aprojected static light pattern allows recovery of their images, a key of thisghost cytometry is to achieve ultrafast cell classification by directlyapplying machine learning methods to the compressive imaging signals in atemporal domain. We show the applicability of our method in the analysis offlowing objects based on the reconstructed images as well as in that based onthe imaging waveform without image production.

Imaging cytometry without image reconstruction (ghost cytometry)