We have recently demonstrated a high throughput three-dimensional (3D) image flow cytometry method, in which a machine-learning algorithm is used to retrieve the 3D refractive index maps of cells from one angle-multiplexing interferogram. Using this system, we have imaged flowing red blood cells and NIH/3T3 cells with a throughput of more than < 10,000 volumes/second. To further demonstrate its potential on cell phenotyping for clinical testing, we plan to apply this platform to image large populations of various cell types and extracting their morphological and biophysical parameters.
Optical diffraction tomography (ODT) has demonstrated its potential for revealing subcellular structures and quantitative compositions in living cells without chemical staining. Recently, we developed a deep-learning based algorithm to reconstruct the 3D refractive index (RI) maps of cells using a single raw interferogram, measured from an angle-multiplexed ODT system. Using this system, we demonstrated a high throughput 3D image cytometry method, in which a microfluidic chip for controlling cell flow is integrated in the ODT system. By flowing the cells in the chip and minimizing the camera exposure time, we can achieve 3D imaging of over 6,000 cells per second.
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