Laser Speckle Contrast Imaging for Capillary Visualization
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Solution Overview
Problem
Laser speckle contrast imaging and optical coherence tomography face challenges in visualizing small blood vessels and capillary blood flows due to background noise from static tissue elements, which obscures the visualization and quantification of dynamic blood perfusion in tissue beds.
Innovation Solution
A method that enhances motion contrast by directing a light beam at blood-perfused tissue, capturing digital images of interference patterns, measuring and comparing light intensities between successive frames to suppress static scattering effects, and compiling an aggregate motion contrast image, thereby improving the visibility and quantification of functional blood vessels, including capillaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laser speckle contrast imaging is used to visualize blood flow, then dynamic blood perfusion can be mapped, but small blood vessels and capillaries cannot be visualized due to background noise from static tissue elements
Solution Approach 1:
The patent extracts and removes the harmful static tissue background signal from the imaging data through specialized algorithms that separate static scattering from dynamic blood flow signals, enabling clear visualization of capillaries and small vessels without background noise interference
Solution Approach 2:
The patent introduces an intermediary processing layer between light interaction with tissue and final image generation, using computational algorithms as mediators to filter out static tissue signals and enhance only the dynamic blood flow components, thereby resolving the contradiction between capturing blood flow and eliminating background noise
2Measurement precision
If optical coherence tomography is used to provide high resolution images, then three-dimensional microvasculature can be extracted, but background noise from stationary tissue background obscures visualization of capillary blood flows
Solution Approach 1:
The patent extracts the harmful stationary tissue background signal from OCT datasets and separates it from the dynamic blood flow signals, enabling clear visualization of capillary networks without obscuring background noise
Solution Approach 2:
The patent employs feedback mechanisms through iterative image processing algorithms that continuously refine the separation of static and dynamic components, enhancing capillary visualization while suppressing background noise through adaptive filtering
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly enhances the visualization and quantification of blood flow within microcirculatory tissue beds, providing clearer images of capillary networks and improved monitoring of wound healing or treatment efficacy without invasive methods.
Implementation Method 1
reflecting the directed light beam off of static target tissue and flowing cells
Implementation Method 2
coherent interference) formed by the coherent addition of scattered laser light propagating within tissue
Implementation Method 3
scattered laser light propagating within tissue
Data Source
AI summary
A method for imaging blood flow through target tissue is disclosed. An example method may include (a) directing a light beam at a blood-perfused target tissue, (b) reflecting the directed light beam off of static target tissue and flowing cells, (c) capturing a plurality of digital images of interference patterns of the reflected light in a plurality of successive frames, (d) measuring a light intensity of at least one pixel of each digital image, where the at least one pixel corresponds to an identical pair of coordinates in each successive frame, (e) comparing the measured light intensity of the at least one pixel of each digital image to the measured light intensity of the at least one pixel of an adjacent frame at the identical pair of coordinates and (f) determining a compiled light intensity for the at least one pixel for an aggregate motion contrast.