Multispectral Retinal Imaging for Deeper Layer Visualization
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Solution Overview
Problem
Current imaging techniques for the retina, such as ophthalmic microscopes and ophthalmoscopes, struggle to provide a comprehensive and accurate representation of the retina, especially in diagnosing eye disorders and during surgery, as they often rely on conventional imaging methods that fail to capture the full spectrum of retinal features.
Innovation Solution
A system that combines multiple images of the retina captured at different electromagnetic wavelengths, using a combination of pixel-wise operations and image processing techniques to enhance visibility of retinal features, including normalization, weighting, and subtraction of images to highlight deeper retinal layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging techniques (digital ophthalmic microscope, multispectral imaging) are used, then imaging of the retina is achieved, but the representation of retinal structures and pathologies is insufficient and inaccurate
Solution Approach 1:
The patent combines multiple images captured at different wavelengths (visible light, infrared, ultraviolet) into a single composite image through pixel-wise operations. This merging of multi-spectral data provides a comprehensive representation of retinal structures and pathologies that cannot be achieved by single-wavelength imaging, directly resolving the contradiction between imaging accuracy and information loss.
Solution Approach 2:
The patent adds spectral dimensionality to conventional two-dimensional retinal images by capturing and processing data across multiple wavelength bands. This dimensional expansion enables visualization of deeper retinal layers and enhanced contrast of pathological features, improving measurement precision while preserving complete retinal information.
2Device complexity
If single-wavelength imaging is used, then imaging process is simple, but visibility of deeper retinal layers and pathological features is limited
Solution Approach 1:
The patent segments the electromagnetic spectrum into multiple wavelength bands (visible, infrared, ultraviolet) and processes each band separately through dedicated image capture and weighting stages. This segmentation allows optimization of imaging parameters for each spectral range, enhancing visibility of specific retinal layers while maintaining manageable process complexity through modular architecture.
Solution Approach 2:
The patent changes the imaging parameters by varying the wavelength of light used for capture and applying different weighting factors to each wavelength channel during image combination. These parameter adjustments enable selective enhancement of deeper retinal structures and pathological features without requiring complete redesign of the imaging system, balancing complexity and precision.
3Loss of information
If multiple wavelength images are combined, then comprehensive retinal representation is achieved, but image processing complexity increases
Solution Approach 1:
The patent implements self-service processing by automatically determining optimal weighting factors for each wavelength channel based on the captured images themselves. The system performs iterative optimization where the image processing algorithm adjusts weights based on image quality metrics, eliminating the need for manual parameter tuning and reducing processing complexity while maintaining information completeness.
Solution Approach 2:
The patent incorporates feedback mechanisms where the image processing system continuously evaluates the quality of combined images and adjusts weighting factors accordingly. This feedback loop enables automatic optimization of the combination process, managing computational complexity through intelligent control while ensuring comprehensive retinal information is preserved in the final image representation.
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
Enhances the visibility of retinal features, allowing for more accurate diagnosis and real-time feedback during surgery, improving the detection of various pathologies and surgical procedures.
Implementation Method 1
measuring (or capturing) light reflected from the retina at different wavelengths or spectral bands across the electromagnetic spectrum
Data Source
AI summary
In certain embodiments, a system, a computer-implemented method, and computer-readable medium are disclosed for enhanced ophthalmic visualization. A plurality of images corresponding to different portions of the electromagnetic spectrum are obtained and combined, such as by pixel-wise subtraction to obtain a combined image. The images may be weighted with weights selected to enhance visualization of features, such as layers of the retina or features corresponding to pathologies. The combined image may be processed, such as by a machine learning model, to extract features.