in vivo laser speckle imaging by adaptive contrast computation for microvasculature assessment

Basak, Kausik; Dey, Goutam; Mahadevappa, Manjunatha; Mandal, Mahitosh; Dutta, Pranab

doi:10.1016/j.optlaseng.2014.05.009

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…These motions are classified as ordered motion (ordered flow of the scatterers) and disordered motion (Brownian motion) which occurs due to the effect of temperature [16]. The dynamic scatterers motion was historically considered to be close to Brownian motion [1], [16], [19], [29], [31]- [33] and with single scattering contribution [34]. This approach was used with the argument that the blood circulation is dense and turbulent [31].…”

Section: Motion Distributionsmentioning

confidence: 99%

“…However, the model that should be used for the autocorrelation function is still a major investigation field in LSCI [1]. Currently, the Lorentzian model is the most used in laser speckle studies [29], [33], [36]- [38] because the ordered flow is approximated by a Lorentzian distribution when scattering is random and uncorrelated [39]. Nevertheless, several authors [16], [40], [41] claim that the Gaussian distribution should be used.…”

Section: Motion Distributionsmentioning

confidence: 99%

“…This paper proposed, and used for the first time, the acronym LASCA to define the analysis of laser speckle images using contrast computation for blood perfusion measurements. Even today, single-exposure LSI is widely used as a research tool because its results, although only quantitative, are conclusive and reliable [33], [65], [76], [77]. Single-exposure LSI has advantages over the traditional blood flow techniques (e.g., LDF), namely its capacity to perform flow maps (2-D images) at a reasonable cost.…”

Section: A Single Exposurementioning

confidence: 99%

See 2 more Smart Citations

Laser Speckle Imaging to Monitor Microvascular Blood Flow: A Review

Vaz

Humeau-Heurtier

Figueiras

et al. 2016

IEEE Rev. Biomed. Eng.

107

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Laser speckle is a complex interference phenomenon that can easily be understood, in concept, but is difficult to predict mathematically, because it is a stochastic process. The use of laser speckle to produce images, which can carry many types of information, is called laser speckle imaging (LSI). The biomedical applications of LSI started in 1981 and, since then, many scientists have improved the laser speckle theory and developed different imaging techniques. During this process, some inconsistencies have been propagated up to now. These inconsistencies should be clarified in order to avoid errors in future works. This review presents a review of the laser speckle theory used in biomedical applications. Moreover, we also make a review of the practical concepts that are useful in the construction of laser speckle imagers. This study is not only an exposition of the concepts that can be found in the literature but also a critical analysis of the investigations presented so far. Concepts like scatterers velocity distribution, effect of static scatterers, optimal speckle size, light penetration angle, and contrast computation algorithms are discussed in detail.

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Section: Motion Distributionsmentioning

confidence: 99%

Section: Motion Distributionsmentioning

confidence: 99%

Section: A Single Exposurementioning

confidence: 99%

See 1 more Smart Citation

Laser Speckle Imaging to Monitor Microvascular Blood Flow: A Review

Vaz

Humeau-Heurtier

Figueiras

et al. 2016

IEEE Rev. Biomed. Eng.

107

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“…The blurred pattern in the RSIs is commonly studied by a contrast analysis, obtaining aContrast Image (CI). The resulting contrast maps associate a relative blood flow value with a contrast value using a point-to-point analysis of the RSI [30,31]. The calculation of the CI is performed by an analysis window that computes the statistical values of a sample in the image.…”

Section: Introductionmentioning

confidence: 99%

Improving Blood Vessel Segmentation and Depth Estimation in Laser Speckle Images Using Deep Learning

Morales-Vargas,

Peregrina-Barreto,

Fuentes-Aguilar

et al. 2024

Information

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Microvasculature analysis is an important task in the medical field due to its various applications. It has been used for the diagnosis and threat of diseases in fields such as ophthalmology, dermatology, and neurology by measuring relative blood flow or blood vessel morphological properties. However, light scattering at the periphery of the blood vessel causes a decrease in contrast around the vessel borders and an increase in the noise of the image, making the localization of blood vessels a challenging task. Therefore, this work proposes integrating known information from the experimental setup into a deep learning architecture with multiple inputs to improve the generalization of a computational model for the segmentation of blood vessels and depth estimation in a single inference step. The proposed R-UNET + ET + LA obtained an intersection over union of 0.944 ± 0.065 and 0.812 ± 0.080 in the classification task for validation (in vitro) and test sets (in vivo), respectively, and a root mean squared error of 0.0085 ± 0.0275 μm in the depth estimation. This approach improves the generalization of current solutions by pre-training with in vitro data and adding information from the experimental setup. Additionally, the method can infer the depth of a blood vessel pixel by pixel instead of in regions as the current state of the art does.

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