Comparison of laser Doppler and laser speckle contrast imaging using a concurrent processing system

Sun, Shen; Hayes‐Gill, Barrie; He, Dahai; Zhu, Yiqun; Huynh, Nam; Morgan, Stephen P.

doi:10.1016/j.optlaseng.2016.02.021

Cited by 15 publications

(9 citation statements)

References 38 publications

(55 reference statements)

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“…However, complications with the software library for the TCP/IP stack, LwIP, slowed the transfer speed to less than a 10th of the theoretical maximum. This data transfer problem is very similar to what Sun et al found in their design . It is possible that these bottlenecks could be reduced by improving the existing interfaces and software libraries used in the transfer between the FPGA and the computer, or by simply using a more powerful FPGA.…”

Section: Discussionsupporting

confidence: 61%

“…Synthetic MELSCI implemented on an FPGA was utilized by Sun et al . They recorded 1024 320 × 320 pixel images at 15 kHz (exposure time 66.6 microseconds) for comparing Laser Doppler Imaging (LDI) and LSCI, or a set of subframes of 1280 × 32 pixels at 11.6 kHz (exposure time 85 microseconds).…”

Section: Discussionmentioning

confidence: 99%

“…One limitation with the SPAD was the very low resolution (64 × 32 pixels). This problem was addressed by Sun et al , who used a high‐speed CMOS sensor coupled with a field programmable gate array (FPGA). Synthetic exposure times were created using the same technique as with the SPAD, in order to compare MELSCI and LDF.…”

Section: Introductionmentioning

confidence: 99%

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

Hultman

Fredriksson

Larsson

et al. 2017

Journal of Biophotonics

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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.

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Section: Discussionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

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

Hultman

Fredriksson

Larsson

et al. 2017

Journal of Biophotonics

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“…6,7 Similar studies have experimentally shown that the LASCA technique displays comparable trends. [8][9][10][11] However, for the LASCA technique, an additional dependency on the image exposure time is introduced.…”

mentioning

confidence: 99%

On the equivalence and differences between laser Doppler flowmetry and laser speckle contrast analysis

Fredriksson

Larsson

2016

J. Biomed. Opt

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, "On the equivalence and differences between laser Doppler flowmetry and laser speckle contrast analysis," J. Abstract. Laser Doppler flowmetry (LDF) and laser speckle contrast analysis (LASCA) both utilize the spatiotemporal properties of laser speckle patterns to assess microcirculatory blood flow in tissue. Although the techniques analyze the speckle pattern differently, there is a close relationship between them. We present a theoretical overview describing how the LDF power spectrum and the LASCA contrast can be calculated from each other, and how both these can be calculated from an optical Doppler spectrum containing various degrees of Doppler shifted light. The theoretical relationships are further demonstrated using time-resolved speckle simulations. A wide range of Monte Carlo simulated tissue models is then used to show how perfusion estimates for LDF and LASCA are affected by changes in blood concentration and speed distribution, as well as by geometrical and optical properties. We conclude that perfusion estimates from conventional single exposure time LASCA are in general more sensitive to changes in optical and geometrical properties and are less accurate in the prediction of real perfusion changes, especially speed changes. Since there is a theoretical one-to-one relationship between Doppler power spectrum and contrast, one can conclude that those drawbacks with the LASCA technique can be overcome using a multiple exposure time setup.

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“…Sun et al 9 showed that the relation between LSCI and LDF is dependent on exposure time, where increasing exposure time increases nonlinearity. Therefore, it seems plausible to assume that multiexposure LSCI (MELSCI) provides a more complete picture that could explain the relation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry

et al. 2019

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Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R ¼ 1.000 for noise-free data, R ¼ 0.993 when a moderate degree of noise is present, and R ¼ 0.995 for in vivo data from an occlusionrelease experiment. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Comparison of laser Doppler and laser speckle contrast imaging using a concurrent processing system

Cited by 15 publications

References 38 publications

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

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

On the equivalence and differences between laser Doppler flowmetry and laser speckle contrast analysis

Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry

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