Contour-Modeled C-Mode OCT Imaging for Eye Curvature Distortion
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Solution Overview
Problem
Conventional optical coherence tomography (OCT) imaging modalities, particularly in ophthalmologic applications, face challenges in accurately interpreting C-mode images due to the natural spherical curvature of the eye and involuntary eye movement, leading to distorted 3D image data and poor quality segmented images, especially in cases with retinal pathologies.
Innovation Solution
A system and method utilizing a computing device with a contour modeling module to superimpose reference anchors on cross-sectional images, generate a line connecting these anchors, sample 3D image data in a variable thickness plane defined by the line, and produce a contour-modeled C-mode image, which provides more accurate visualization by compensating for distortions caused by the eye's curvature and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional C-mode imaging is used to visualize eye structures, then the imaging process is simple and fast, but the images are distorted due to spherical curvature and eye movement, making accurate interpretation difficult
Solution Approach 1:
The system performs preliminary actions by detecting eye movement during the scan and calculating distortion parameters before generating the final C-mode image. This allows the system to pre-compensate for spherical curvature and movement artifacts, improving image accuracy without adding significant complexity to the final output
Solution Approach 2:
The patent introduces an intermediary processing step that acts as a mediator between raw OCT data and conventional C-mode images. This intermediary layer applies distortion correction algorithms and spherical compensation techniques, enabling accurate image generation while maintaining a relatively simple overall system architecture
2Measurement precision
If segmentation algorithms are used to improve image quality, then visualization accuracy improves, but the algorithms frequently fail to detect proper structures in cases with retinal pathologies
Solution Approach 1:
The system converts the harmful effect of retinal pathologies (which cause segmentation failures) into a benefit by using distortion detection to identify and correct imaging artifacts. By detecting abnormal patterns caused by both pathologies and spherical distortion, the system can differentiate between true pathology and imaging artifacts, improving both accuracy and reliability
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors image quality and distortion parameters during scanning. This feedback allows real-time adjustment of imaging parameters and correction algorithms, enabling the system to adapt to varying pathological conditions and maintain reliable detection across different cases
3Productivity
If 3D image data is acquired rapidly using SD-OCT, then scanning speed increases, but eye movement during scanning introduces distortion that degrades image quality
Solution Approach 1:
The system performs preliminary detection of eye movement and distortion parameters during the rapid SD-OCT scan. By detecting and characterizing movement artifacts in real-time, the system can apply corrective transformations before final image reconstruction, maintaining both high scanning speed and image quality
Solution Approach 2:
The patent introduces dynamic correction that adapts to changing eye movement conditions during scanning. The system continuously updates distortion parameters and adjustment factors based on real-time monitoring, enabling it to compensate for varying movement patterns while maintaining rapid acquisition speeds
Data Source
AI summary
A system. The system includes a computing device configured for communication with an imaging system and with a display device. The computing device includes a contour modeling module. The contour modeling module is configured for superimposing reference anchors on a cross-sectional image generated from 3D image data, for generating a line which connects the reference anchors, for sampling the 3D image data in a variable thickness plane defined by the connecting line, and for generating a contour-modeled C-mode image from the sampled 3D image data.