Phase Variance OCT for Transverse Flow Detection
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Solution Overview
Problem
Current optical coherence tomography (OCT) systems face limitations in visualizing flow, particularly in regions where the flow is perpendicular to the imaging direction, due to high phase noise and limited statistics, which restricts the visualization of slow or transverse flows, and existing methods for motion contrast are inadequate for diagnosing and treating diseases.
Innovation Solution
The method involves acquiring and analyzing phase variance and intensity fluctuation data to produce motion contrast, allowing for the identification and characterization of mobile scatterers and flow regions, independent of orientation, using spectral domain optical coherence tomography (SDOCT) and incorporating techniques like bulk motion removal and phase variance calculations to enhance contrast and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Doppler OCT technique is used to visualize flow, then axial flow can be detected, but transverse flow and slow flow visualization is limited due to phase noise and limited statistics
Solution Approach 1:
The patent changes the measurement parameter from phase change (Doppler OCT) to phase variance and intensity fluctuation. This parameter transformation enables detection of transverse flow and slow flow that were previously undetectable due to phase noise limitations. The phase variance metric is insensitive to the phase noise that limits conventional Doppler OCT, thereby resolving the contradiction between measurement precision and detection difficulty.
Solution Approach 2:
The patent introduces phase variance as an intermediary measurement that mediates between the limited phase change information and the need for accurate flow detection. By using phase variance calculated from multiple A-scans as the intermediary metric, the system can detect transverse flow and slow flow without being limited by the phase noise that plagues direct phase change measurements.
2Productivity
If only a few successive depth reflectivity measurements (A-scans) are used to maintain fast imaging speeds, then imaging speed is maintained, but minimum observable axial flow is limited
Solution Approach 1:
The patent applies continuity of useful action by continuously acquiring multiple A-scans at the same transverse location (M-scan configuration) to accumulate statistics for phase variance calculation. This continuous acquisition of useful data enables accurate flow detection while maintaining fast imaging speeds, as the phase variance metric efficiently utilizes the accumulated data without requiring excessive averaging.
Solution Approach 2:
By changing from phase change measurement to phase variance measurement, the patent enables accurate flow detection with fewer A-scans. The phase variance parameter is more robust to noise and requires fewer measurements to achieve reliable statistics, thereby maintaining fast imaging speeds while improving minimum observable axial flow detection.
3Measurement precision
If phase sensitive analysis is used for vascular visualization, then axial flow can be measured, but flow perpendicular to imaging direction cannot be visualized due to cos θ approaching zero
Solution Approach 1:
The patent transitions from one-dimensional phase change measurement (sensitive only to axial flow) to two-dimensional phase variance analysis that captures flow in multiple directions. By analyzing phase variance in conjunction with intensity fluctuation and speckle information, the system gains sensitivity to transverse flow components that were previously invisible, thereby expanding adaptability to different flow directions while maintaining axial flow measurement precision.
Solution Approach 2:
The patent changes the measurement parameter from phase change (which has cos θ dependency) to phase variance combined with intensity fluctuation analysis. This parameter transformation removes the cos θ limitation, enabling detection of flow in any direction including transverse flow perpendicular to the imaging direction, while preserving the ability to measure axial flow with high precision.
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 enables the visualization of nanometer-scale motion, improves the detection of intermittent blood flow, and provides quantitative flow estimation, enhancing diagnostic capabilities and treatment options by providing detailed three-dimensional visualization of vascular regions.
Implementation Method 1
OCT is a non-invasive optical imaging technique which produces depth-resolved reflectance imaging of samples through the use of a low coherence interferometer system
Implementation Method 2
Phase is a type of high resolution position measurement of a reflection along the optical path length of the imaging system, which is cyclic of the frequency of half the wavelength of the imaging light. Changes in phase are proportional to the axial flow
Implementation Method 3
The phase variance contrast utilizes the temporal evolution of the measured phase variance of the motion to identify and characterize mobile scatterers within the OCT sample images. The motion contrast, and in particular the phase variance contrast is able to observe the nanometer scale motion of scatterers
Implementation Method 4
The methods and techniques developed herein demonstrate motion contrast within optical coherence tomography images. The temporal fluctuations in intensity of the OCT images can also be used as another form of contrast to observe the fluctuations associated with flow and absorption changes
Data Source
AI summary
The methods described herein are methods to ascertain motion contrast within optical coherence tomography data based upon phase variance. The phase variance contrast observes the nanometer scale motion of scatterers associated with Brownian motion and other non-flow motion. The inventive method of calculating motion contrast from the phase variance can differentiate regions of different mobility based on the motion contrast differences, and can use the phase information to characterize mobility properties of the scatterers. In flow regions, the inventive method for acquiring and analyzing motion contrast can identify the regions as well as characterize the motion. Furthermore, the inventive method can determine quantitative flow estimation, the index of refraction variations, and absorption variations within flow regions.