Optoretinography Phase-Velocity Processing for Retinal Boundary Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optical coherence tomography (OCT) systems face challenges in accurately identifying retinal layer boundaries, particularly in cases with poor axial resolution or noisy data, and conventional methods rely on intensity peak identification, which can be unreliable.
Innovation Solution
A computer-implemented method processes phase components of OCT images to calculate a velocity profile, compensating for bulk motion, and uses algorithms like PCA to determine retinal layer boundaries based on velocity variations, eliminating the need for intensity peak detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If intensity peak identification is used to determine retinal layer boundaries, then the method is simple to implement, but the measurement precision deteriorates in cases with poor axial resolution or noisy data
Solution Approach 1:
The patent changes the parameter used for boundary identification from intensity (amplitude) to velocity (phase derivative). By computing the temporal derivative of the phase component, the system transforms static intensity information into dynamic velocity information, which provides more reliable boundary detection in noisy or low-resolution conditions. This parameter transformation resolves the contradiction by maintaining implementation simplicity while significantly improving measurement precision.
2Device complexity
If conventional intensity-based methods are used, then no additional processing is needed, but the reliability deteriorates when axial resolution is poor or data is noisy
Solution Approach 1:
The patent substitutes the mechanical/intensity-based detection approach with a phase-based approach. Instead of relying on amplitude peaks from optical intensity measurements, the system uses phase information and its temporal derivative (velocity) to identify boundaries. This substitution increases processing complexity slightly but dramatically improves reliability, especially in challenging imaging conditions with poor axial resolution or noise.
3Measurement precision
If phase processing is added to improve boundary identification, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary phase compensation for bulk motion before computing the velocity profile. By pre-compensating for eye movements and other bulk motions using registration techniques, the system prepares the data in advance, making the subsequent velocity-based boundary detection more accurate. This preliminary action justifies the added processing complexity by significantly improving measurement precision and reliability.
Solution Approach 2:
The patent introduces velocity as an intermediary parameter between phase and boundary position. Instead of directly using phase or intensity, the system computes the temporal derivative of phase to obtain velocity, then uses velocity peaks to identify boundaries. This intermediary transformation simplifies the relationship between phase changes and boundary positions, improving accuracy while keeping the processing pipeline manageable.
4Measurement precision
If adaptive optics or tracking systems are used to improve ORG response extraction, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent substitutes complex mechanical systems (adaptive optics and tracking systems) with a computational approach based on phase-velocity analysis. By using velocity profiles derived from phase components, the system can extract ORG responses without requiring expensive and complex hardware interventions. This substitution maintains measurement precision while dramatically reducing device complexity and cost.
Solution Approach 2:
The patent enables the OCT system to self-correct for motion artifacts and self-identify boundaries through intrinsic phase information, without requiring external tracking systems or adaptive optics. The velocity-based method uses the phase data already captured by the OCT system itself, allowing the system to serve its own motion compensation and boundary detection needs, thereby eliminating the need for additional complex subsystems.
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 allows reliable identification of retinal layer boundaries, even in low-resolution OCT data, enabling effective ORG responses to be extracted from various retinal regions without requiring adaptive optics or tracking systems.
Implementation Method 1
Optical coherence tomography (OCT) is an imaging technique based on low-coherence interferometry
Implementation Method 2
acquire high-resolution two- and three-dimensional images of optical scattering media, such as biological tissue
Implementation Method 3
a spectral interferogram resulting from an interference between light in the reference arm and light in the sample arm of the interferometer at each A-scan location is Fourier transformed
Implementation Method 4
OCT imaging systems can also be classified as being phase-resolved, where both the intensity and phase of the light reflected from the imaging target are measured as a function of axial depth
Implementation Method 5
processing the phase component of the first OCT image and the phase component of the second OCT image to calculate a velocity profile indicative of a distribution, along the axial direction, of velocity within the common portion of the retina
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A computer-implemented method of processing respective phase components of a first OCT image and a second OCT image of a sequence of OCT images of a common portion of a retina acquired by a Fourier-domain OCT imaging system after stimulation of the common portion by an optical stimulus, the common portion comprising a layer of the retina whose thickness changed during acquisition of the sequence of OCT images, to determine an indication of a position along an axial direction in the OCT images of a boundary of the layer, the method comprising: processing the phase component of the first OCT image and the phase component of the second OCT image to calculate a velocity profile indicative of a distribution, along the axial direction, of velocity within the common portion of the retina; and determining the indication based on the calculated velocity profile.