Fractality of pulsatile flow in speckle images

Nemati, M.; Kenjereš, Saša; Urbach, H. P.; Bhattacharya, Nandini

doi:10.1063/1.4948297

Cited by 4 publications

(7 citation statements)

References 33 publications

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“…Our group has previously applied the principles outlined in this paper to extract the heartbeat in an experimental setup [21]. The heartbeat was also measured in vivo using this technique [29]. Our simulations compare well with those experimental results.…”

Section: B Extracting a Heartbeatsupporting

confidence: 54%

“…Since blurring decreases σ I , but does not affect I , the result is a lower C. Therefore, through the camera integration time, the speckle contrast depends on the amount of blurring and thus also on velocity [19]. Our research group has applied this principle previously to experimentally study pulsatile flow in a patient-specific carotid artery [21,29].…”

Section: Figmentioning

confidence: 99%

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Laser speckle imaging of flowing blood: A numerical study

Boterman

Kleijn

et al. 2019

Phys. Rev. E

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Laser speckle imaging (LSI) can be used to study dynamic processes in turbid media, such as blood flow. However, it is presently still challenging to obtain meaningful quantitative information from speckle, mainly because speckle is the interferometric summation of multiply scattered light. Consequently, speckle represents a convolution of the local dynamics of the medium. In this paper, we present a computational model for simulating the LSI process, which we aim to use for improving our understanding of the underlying physics. Thereby reliable methods for extracting meaningful information from speckle can be developed. To validate our code, we apply it to a case study resembling blood flow: a cylindrical fluid flow geometry seeded with small spherical particles and modulated with a heartbeat signal. From the simulated speckle pattern, we successfully retrieve the main frequency modes of the original heartbeat signal. By comparing Poiseuille flow to plug flow, we show that speckle boiling causes a small amount of uniform spectral noise. Our results indicate that our computational model is capable of simulating LSI and will therefore be useful in future studies for further developing LSI as a quantitative imaging tool.

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Section: B Extracting a Heartbeatsupporting

confidence: 54%

Section: Figmentioning

confidence: 99%

Laser speckle imaging of flowing blood: A numerical study

Boterman

Kleijn

et al. 2019

Phys. Rev. E

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“…Recently, a fractal-based approach was employed to analyze speckle statistics due to its ability to quantify self-similar properties [13,14]. Furthermore, the roughness and the texture information of speckle images are captured by frequency response [15][16][17].…”

Section: Introductionmentioning

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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“…4,5 A recent paper has used optical speckle image velocimetry to quantitatively reconstruct the velocity profile in blood vessels, 6 but their work is still invasive in nature. However, when using speckle patterns, no invasive imaging is necessary for the specific case of the carotid artery, 7 largely simplifying the required equipment. The latter makes speckle decorrelation a promising candidate as a technique to study the flow of turbid media, [8][9][10] such as blood.…”

Section: Introductionmentioning

confidence: 99%

Toward detecting atherosclerosis using dynamic laser speckle contrast imaging: A numerical study

Dellevoet

Boterman

et al. 2022

Journal of Applied Physics

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The disease atherosclerosis causes stenosis inside the patient’s arteries, which often eventually turns lethal. Our goal is to detect a stenosis in a non-invasive manner, preferably in an early stage. To that end, we study whether and how laser speckle contrast imaging (LSCI) can be deployed. We start out by using computational fluid dynamics on a patient-specific stenosed carotid artery to reveal the flow profile in the region surrounding the stenosis, which compares well with particle image velocimetry experiments. We then use our own fully interferometric dynamic light scattering routines to simulate the process of LSCI of the carotid artery. Our approach offers an advantage over the established Monte Carlo techniques because they cannot incorporate dynamics. From the simulated speckle images, we extract a speckle contrast time series at different sites inside the artery, of which we then compute the frequency spectrum. We observe an increase in speckle boiling in sites where the flow profile is more complex, e.g., containing regions of backflow. In the region surrounding the stenosis, the measured speckle contrast is considerably lower due to the higher local velocity, and the frequency signature becomes notably different with prominent higher-order frequency modes that were absent in the other sites. Although future work is still required to make our new approach more quantitative and more applicable in practice, we have provided a first insight into how a stenosis might be detected in vivo using LSCI.

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Fractality of pulsatile flow in speckle images

Cited by 4 publications

References 33 publications

Laser speckle imaging of flowing blood: A numerical study

Laser speckle imaging of flowing blood: A numerical study

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

Toward detecting atherosclerosis using dynamic laser speckle contrast imaging: A numerical study

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