Real-time video-rate perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

ABSTRACT: Multi-exposure laser speckle contrast imaging (MELSCI) estimates microcirculatory blood perfusion more accurately than single-exposure LSCI. However, the technique has been hampered by technical limitations due to massive data throughput requirements and nonlinear inverse search algorithms, limiting it to an offline technique where data must be postprocessed. To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real-time video-rate perfusion imaging with high accuracy. The MELSCI algorithm was implemented in programmable hardware (field programmable gate array) closely interfaced to a high-speed CMOS sensor for real-time calculation. Perfusion images were estimated in real-time from the MELSCI data using an artificial neural network trained

SUBMITTER: Hultman M 

PROVIDER: S-EPMC7666876 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature