Abstract
We present a fast label-free computational flow cytometer based on a strategy of compressive imaging. Scattered light from flowing objects is sub-divided into user-defined basis patterns by a deformable mirror and routed to different detectors associated with each pattern. The patterns can be optimized to be matched to the object features of interest, thus facilitating object identification and separation. Compared to conventional scanning flow cytometers, our technique provides increased information capacity without sacrificing flow velocity. Unique features of our matched-filter strategy are that it can simultaneously probe multiple objects throughout large fields of view with long depths of field. In our proof-of-concept demonstrations, we achieve throughputs of over 10,000 particles/s, working at flow velocities of over 1m/s.