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

Martin Hultman, Marcus Larsson, Tomas Strömberg, Ingemar Fredriksson, Martin Hultman, Marcus Larsson, Tomas Strömberg, Ingemar Fredriksson

Abstract

Significance: 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.

Aim: To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real-time video-rate perfusion imaging with high accuracy.

Approach: 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 on simulated data. The MELSCI perfusion was compared to two existing single-exposure metrics both quantitatively in a controlled phantom experiment and qualitatively in vivo.

Results: The MELSCI perfusion shows higher signal dynamics compared to both single-exposure metrics, both spatially and temporally where heartbeat-related variations are resolved in much greater detail. The MELSCI perfusion is less susceptible to measurement noise and is more linear with respect to laser Doppler perfusion in the phantom experiment (R2 = 0.992).

Conclusions: The presented MELSCI system allows for real-time acquisition and calculation of high-quality perfusion at 15.6 frames per second.

Keywords: laser Doppler; laser speckle contrast analysis; laser speckle contrast imaging; microcirculation; multi-exposure laser speckle contrast imaging; perfusion.

Figures

Fig. 1
Fig. 1
Visualization of the system flow from the Lux1310 camera sensor, through the multi-exposure contrast algorithm in the FPGA, and noise correction and perfusion models in the computer.
Fig. 2
Fig. 2
(a) Image of PANN at the beginning of the experiment, chosen at 4 min in (b). The ROI used to create (b)–(e) is also shown. (b) PANN, PLDF, and temperature of the milk phantom as a function of time. (c)–(e) Regression plots of the three camera-based perfusion estimates against PLDF. The blue lines correspond to a perfect agreement with PLDF. The data were normalized to the average perfusion at 25°C.
Fig. 3
Fig. 3
PANN, PK2(8  ms), and PK(8  ms) during an occlusion-release provocation of the right forearm of a healthy subject. Data on the full timescale have been averaged with a 2-s moving window to remove heart beats. The data in the inlays were not averaged and show a clear heartbeat signal during baseline and reperfusion; gridlines in the inlay plots indicate 1-s intervals. PK2 and PK were normalized to have the same baseline average as PANN (0:00 to 5:00 min).
Fig. 4
Fig. 4
(a) Images of PANN; (b)–(d) images of PK2(1  ms), PK2(8  ms), and PK2(64  ms); and (e) images of PK(8  ms) at the three phases of the occlusion-release provocation. The columns correspond to baseline (selected at 4:00), occlusion (selected at 8:00), and reperfusion (selected at 10:06), respectively. Each image was averaged over 2 s (31 images). The PK2 and PK images have been individually scaled to match the average perfusion of the corresponding PANN image in a large ROI. This highlights the structural differences between the three techniques rather than the absolute values of the perfusion estimates. Note that the color scale for the occlusion phase is different from the other phases to make the vascular structures more prominent (Video S1, MP4, 45.8 MB [URL: https://doi.org/10.1117/1.JBO.25.11.116007.1]).

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