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

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.

SUBMITTER: Hultman M

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

REPOSITORIES: biostudies-literature

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

Hultman Martin M Larsson Marcus M Strömberg Tomas T Fredriksson Ingemar I

Journal of biomedical optics 20201101 11

<h4>Significance</h4>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.<h4>Aim</h4>To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real ...[more]

PMID: 33191685

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Project description:BackgroundIntestinal resection causes inevitable vascular damage, which cannot always be seen during an intraoperative clinical assessment of local intestinal perfusion. If left unaltered, impaired perfusion can lead to complications, such as anastomotic leakage (AL). Therefore, we demonstrate the use of a novel laparoscopic laser speckle contrast imaging (LSCI)-based approach in order to assess local intestinal perfusion during the construction of intestinal anastomoses.MethodsThree segments were isolated from the small intestine of a pig, while the perfusion of each was compromised by coagulating 7-8 mesenteric arteries. Both clinical assessments and LSCI were used to detect the induced perfusion deficits and to subsequently guide a transection in either a well perfused, marginally perfused, or poorly perfused tissue area within the segment. Bowel ends were then utilized for the creation of three differently perfused anastomoses: well perfused/well perfused (anastomosis segment 1), well perfused/poorly perfused (anastomosis segment 2), and poorly perfused/poorly perfused (anastomosis segment 3). After construction of the anastomoses, a final perfusion assessment using both clinical assessment and LSCI was executed in order to evaluate the vascular viability of the anastomosis.ResultsLaparoscopic LSCI enabled continuous assessment of local intestinal perfusion and allowed for detection of perfusion deficits in real time. The imaging feedback precisely guided the surgical procedure, and, when evaluating the final anastomotic perfusion, LSCI was able to visualize the varying degrees of perfusion, whereas standard clinical assessment yielded only minor differences in visual appearance of the tissue.ConclusionsIn this technical note, we demonstrate a novel LSCI-based approach for intraoperative perfusion assessment. With its ability to continuously visualize perfusion in real time, laparoscopic LSCI has significant potential for the optimization of anastomotic surgery in the near future.

Dataset Information

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

Publications

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

Similar Datasets

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