A hardware accelerated approach for imaging flow cytometry

Abstract

Imaging flow cytometry uses high-speed flows and a camera to capture morphological features of hundreds to thousands of cells per second. These morphological features can be useful to isolate sub-populations of cells for life science research and diagnostics. Our experimental setup utilizes a high speed 208×32 resolution CMOS camera, operating at over 140,000 frames per second (FPS). In each frame, the analysis routine detects the presence of an object, and performs morphology measurements. Real-time cell sorting requires a latency under 10 ms in addition to a throughput of 140,000 FPS. In this paper, we will describe GPU and FPGA accelerated implementations of the image analysis necessary for an automated cell sorting system. Our FPGA design results in a 38× speedup over software, providing 2,262 FPS with 11.9 ms of latency. Our GPU implementation shows a 22× speedup, supporting 1,318 FPS with 152 ms of latency.