Multispectral OCT Retinal Imaging for Amyloid Plaque Detection
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Solution Overview
Problem
Current imaging technologies, such as traditional OCT and fundus imaging, are unable to visualize amyloid-beta plaques in the retina without the use of specialized contrast agents and have not been successfully applied in live humans, limiting the ability to image these plaques in the retina or brain.
Innovation Solution
The use of optical coherence tomography (OCT) combined with multispectral/hyperspectral imaging, spectral wavelength selection, and image processing, along with the application of contrast agents like curcumin, to identify and visualize amyloid-beta plaques in the retina by obtaining their spectral signature and determining their location in discrete retinal layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional OCT and fundus imaging techniques are used, then the imaging process is simple and non-invasive, but amyloid-beta plaques cannot be visualized in the retina
Solution Approach 1:
The patent combines OCT imaging with multispectral/hyperspectral imaging capabilities into a single integrated system. This merging allows the system to capture both structural information from OCT and spectral signature information from multispectral imaging, enabling amyloid-beta plaque visualization without requiring separate imaging sessions or procedures.
Solution Approach 2:
The system utilizes spectral wavelength selection to identify specific wavelength ranges where amyloid-beta plaques exhibit characteristic absorption or reflection patterns. By analyzing the spectral signature across multiple wavelengths, the system can distinguish amyloid plaques from other retinal structures, thereby improving visualization capability through parameter-based differentiation.
2Reliability
If specialized contrast agents are used to visualize amyloid-beta plaques, then plaque visualization becomes possible, but the procedure becomes more complex and requires additional substances
Solution Approach 1:
The system exploits the intrinsic spectral properties of amyloid-beta plaques themselves to generate contrast. By identifying specific wavelength ranges where amyloid exhibits characteristic optical signatures, the system enables self-contrasting without requiring external contrast agents. This self-service approach maintains high detection accuracy while simplifying the imaging procedure.
Solution Approach 2:
The multispectral imaging system detects variations in optical properties across different wavelength ranges, effectively creating spectral 'color' signatures that differentiate amyloid-beta plaques from surrounding retinal tissue. This spectral differentiation allows accurate plaque detection based on inherent optical characteristics rather than exogenous contrast agents.
3Adaptability or versatility
If amyloid-beta plaques are imaged in live humans, then clinical applicability is achieved, but the imaging technique has not been successfully performed previously
Solution Approach 1:
The imaging system is designed to perform multiple functions: structural imaging via OCT, spectral analysis via multispectral imaging, and plaque identification through integrated image processing. This multi-functionality allows the system to adapt to clinical requirements while maintaining reliable plaque detection in live human subjects.
Solution Approach 2:
The system employs sophisticated image processing algorithms as intermediaries to bridge the raw imaging data and clinically actionable information. These processing steps extract spectral signatures, segment retinal layers, and identify plaque locations, thereby ensuring reliable detection while adapting the technique for clinical use in live humans.
4Measurement precision
If spectral analysis and image processing are applied to identify amyloid plaques, then plaque identification accuracy improves, but data processing complexity increases
Solution Approach 1:
The image processing pipeline segments the retinal structure into discrete layers and identifies specific regions containing amyloid-beta plaques. By dividing the complex retinal architecture into manageable segments and analyzing spectral characteristics within each segment, the system achieves high identification accuracy while organizing processing tasks in a systematic manner.
Solution Approach 2:
The system extracts specific spectral signature features from the multispectral data that are characteristic of amyloid-beta plaques. By isolating and analyzing only the relevant spectral features rather than processing the entire dataset, the system maintains high identification accuracy while reducing unnecessary processing complexity.
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
Enables the visualization and identification of amyloid-beta plaques in the retina, allowing for the assessment of plaque load and disease severity, which was previously unattainable, and can be used with or without contrast agents, providing a non-invasive method for in vivo imaging in humans.
Implementation Method 1
The present invention can be utilized in an OCT system that utilizes optical coherence tomography (OCT)
Implementation Method 2
curcumin reflectance and/or fluorescence imaging
Implementation Method 3
curcumin reflectance and/or fluorescence imaging
Data Source
AI summary
An apparatus to produce an OCT of an eye or a brain of a patient to identify one or more plaques in a plurality of discrete OCT retinal layers. The apparatus also includes a plurality of methods for identifying one or more plaques in a plurality of discrete OCT retinal layers that can include a contrast agent and a normative database.