Velocity measurements of heterogeneous RBC flow in capillary vessels using dynamic laser speckle signal

Li, Chenxi; Wang, Ke

doi:10.1117/1.jbo.22.4.046002

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…We used detrended fluctuation analysis to calculate fractal dimension [33,34] with a second-order polynomial regressor. The fractal dimension D, is calculated using Hurst exponent α given in equation (2.1), where α is the slope of the double logarithmic plot, log(F) versus log(c), where F represents Fluctuations and c represents the number of segments [35].…”

Section: Feature Extractionmentioning

confidence: 99%

Machine learning integrated laser speckle image analysis for the simultaneous extraction of flow and scatterer concentration from capillary phantoms

Hegde

Sujatha

2023

Eng. Res. Express

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Laser speckle imaging is one of the powerful non-invasive imaging techniques to monitor and assess microcirculation parameters. Qualitative analysis of perfusion parameters has been carried out in the recent past. But the quantitative estimation of tissue perfusion parameters like flow velocity and scatterer concentration simultaneously from laser speckle images remains challenging. The introduction of machine learning methods into laser speckle image analysis can help meet these challenges to a great extent. This paper presents an approach for the simultaneous extraction of perfusion parameters, using multi-target regression techniques applied to the extracted features from acquired laser speckle images after Eigendecomposition filtering. The multi-target regression trees are identified as an effective tool for the simultaneous extraction of flow velocity and scatterer concentration with adequate mean absolute percentage error. Besides the achieved speed and computational efficiency, our work demonstrates the viability of this approach in quantifying perfusion parameters simultaneously. Due to its simple, non-invasive, and cost-effective nature, the proposed technique could be used in the realtime assessment of tissue health.

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Section: Feature Extractionmentioning

confidence: 99%

Machine learning integrated laser speckle image analysis for the simultaneous extraction of flow and scatterer concentration from capillary phantoms

Hegde

Sujatha

2023

Eng. Res. Express

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“…However, both LSCI and DLSI present the challenges of measuring the absolute speed of moving blood cells [18,19]. To obtain the absolute velocity of blood flow, several particle image velocimetry (PIV) algorithms [20][21][22] have been developed to track the movement of blood cells, in consecutive reflectance or fluorescence image frames recorded by various imaging modalities, such as wide-field microscopy [23][24][25], confocal microscopy [26,27], two photon microscopy [28,29], and optical coherence tomography [30]. Correlation PIV calculates the spatial cross-correlation of the light intensity between consecutive frames within a local window, to obtain the speed and the direction of the moving red blood cells.…”

Section: Introductionmentioning

confidence: 99%

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Meng

Huang

Feng

et al. 2023

IJMS

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The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

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“…Various tools have been developed to measure and monitor the spatiotemporal dynamics of CBF, as CBF regulation provides the key to unravel the coupling of local blood flow control in response to the nearby neuronal activity [8][9][10]. Optical methods for CBF measurements fall under three categories: 1) Doppler based methods, such as laser Doppler flowmetry, Doppler optical coherence tomography, and photoacoustic Doppler velocimetry [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Quantitative blood flow estimationin vivoby optical speckle image velocimetry

Qureshi

Liu

Mac

et al. 2021

Preprint

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Speckle based methods are popular non-invasive, label-free full-field optical techniques for imaging blood flow maps at single vessel resolution with a high temporal resolution. However, conventional speckle approach cannot provide an absolute velocity map with magnitude and direction. Here, we report a novel optical speckle image velocimetry (OSIV) technique for measuring the quantitative blood flow vector map by utilizing particle image velocimetry with speckle cross-correlations. We demonstrate that our OSIV instrument has a linearity range up to 7 mm/s, higher than conventional optical methods. Our method can measure the absolute flow vector map at up to 190 Hz without sacrificing the image size, and it eliminates the need for a high-speed camera/detector. We applied OSIV to image the blood flow in a mouse brain, and as a proof of concept, imaged the real-time dynamic changes in the cortical blood flow field during the stroke process in vivo. Our wide-field quantitative flow measurement OSIV method without the need of tracers provides a valuable tool for studying the healthy and diseased brain.

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Velocity measurements of heterogeneous RBC flow in capillary vessels using dynamic laser speckle signal

Cited by 7 publications

References 19 publications

Machine learning integrated laser speckle image analysis for the simultaneous extraction of flow and scatterer concentration from capillary phantoms

Machine learning integrated laser speckle image analysis for the simultaneous extraction of flow and scatterer concentration from capillary phantoms

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Quantitative blood flow estimationin vivoby optical speckle image velocimetry

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