A 15.6 frames per second 1‐megapixel multiple exposure laser speckle contrast imaging setup

Abstract

A multiple exposure laser speckle contrast imaging (MELSCI) setup for visualizing blood perfusion was developed using a field programmable gate array (FPGA), connected to a 1000 frames per second (fps) 1-megapixel camera sensor. Multiple exposure time images at 1, 2, 4, 8, 16, 32 and 64 milliseconds were calculated by cumulative summation of 64 consecutive snapshot images. The local contrast was calculated for all exposure times using regions of 4 × 4 pixels. Averaging of multiple contrast images from the 64-millisecond acquisition was done to improve the signal-tonoise ratio. The results show that with an effective implementation of the algorithm on an FPGA, contrast images at all exposure times can be calculated in only 28 milliseconds. The algorithm was applied to data recorded during a 5 minutes finger occlusion. Expected contrast changes were found during occlusion and the following hyperemia in the occluded finger, while unprovoked fingers showed constant contrast during the experiment. The developed setup is capable of massive data processing on an FPGA that enables processing of MELSCI data in 15.6 fps (1000/64 milliseconds). It also leads to improved frame rates, enhanced image quality and enables the calculation of improved microcirculatory perfusion estimates compared to single exposure time systems.

Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup

“…As we can see in Figure 2, the provoked finger has a more than six-fold increase in perfusion compared to the baseline. This measurement is shown primarily as a way to compare these results with our previous publication 8 , where we presented multi-exposure contrast images of a hand, and not perfusion. It also serves as a qualitative measure of how visible the perfusion change is for both the occlusion and reperfusion.…”

“…In addition, data from an occlusion-release provocation of a finger was analyzed. That data was collected during a previous study 8 .…”

“…To overcome this, novel approaches based on a high-speed camera connected to a dedicated FPGA processing unit have been proposed 8,9 . We have previously demonstrated an implementation of such a system, where real-time MELSCI data is achieved by cumulative summation of images from a 1000 frames-per-second camera using a fix exposure time of 1 ms. 8 Initial measurements captured during an occlusion-release provocation of a finger indicated that this system can be used for a fast and accurate acquisition of MELSCI data.…”

“…Flow chart of the processing pipeline, from camera to calculated perfusion images and graphs. The left side of the dotted line is the real-time FPGA system and camera described in our previous publication8 , and the right side is performed in the computer as post processing.…”

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

“…To address this problem of a data and processing bottleneck, we have previously presented a system for synthetic MELSCI, 14 based on a high-speed 1000-frames per second (fps) 1-megapixel camera directly interfaced to a field programmable gate array (FPGA) performing the multi-exposure contrast algorithm outlined by Dragojević 13 in real-time. Processed contrast images were sent to a computer, massively reducing the data throughput requirements.…”