Nerve Imaging Background Subtraction via Dual Spectral Segmentation
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Solution Overview
Problem
Current fluorescence imaging systems face challenges in suppressing unintentionally labeled background tissues during nerve imaging, leading to poor signal-to-background ratio and limited dynamic range, which can result in iatrogenic nerve damage during surgical procedures.
Innovation Solution
The system employs a dual spectral region approach with a processing unit to separate and subtract background fluorescence, using a targeted nerve imaging agent that selectively binds to myelin basic protein, and utilizes a dichroic beam splitter and signal detectors to collect and process fluorescence emissions from different spectral regions, allowing for the generation of high-quality, fat-suppressed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fluorescence imaging methods are used, then fluorescence images can be generated, but the signal-to-background ratio is poor and background tissues cannot be effectively suppressed
Solution Approach 1:
The fluorescence emission spectrum is segmented into multiple spectral regions (e.g., first spectral region and second spectral region) using beam splitters. By separating the spectrum into distinct regions, the system can selectively detect and process signals from different wavelength ranges, enabling differentiation between target nerve tissue fluorescence and background tissue fluorescence, thereby improving the signal-to-background ratio
Solution Approach 2:
The system changes the spectral detection parameters by acquiring fluorescence images at multiple different spectral regions or wavelengths. By varying the detection wavelength parameter across multiple measurements, the system can identify and suppress background signals while preserving target signals, as background tissues typically have different spectral signatures than target nerves
2Reliability
If fluorophores are designed to label specific targets, then target visualization is improved, but nonspecific binding occurs leading to poor SBR
Solution Approach 1:
The system converts the harmful effect of nonspecific fluorophore binding to background tissues into a beneficial diagnostic opportunity. By acquiring fluorescence images at multiple spectral regions, the system can identify background tissue signals as artifacts and subtract them, thereby converting the initial poor SBR caused by nonspecific binding into an improved SBR through computational processing
Solution Approach 2:
The system introduces an intermediary processing step between fluorescence acquisition and image interpretation. By inserting spectral region separation and background subtraction algorithms as intermediaries, the system can distinguish between specific target labeling and nonspecific background binding, effectively mediating the relationship between fluorophore administration and final image quality
3Measurement precision
If multiple spectral regions are acquired and processed, then background suppression is improved, but system complexity increases
Solution Approach 1:
The optical path is segmented using beam splitters to direct different spectral regions to separate detectors or detection channels. This segmentation allows parallel acquisition of multiple spectral images, improving background suppression capability while distributing the complexity across separate, manageable optical paths rather than requiring a single complex system
Solution Approach 2:
The system adds a spectral dimension to the traditional two-dimensional spatial image acquisition. By acquiring fluorescence signals across multiple wavelength dimensions (spectral regions) in addition to spatial dimensions, the system gains an extra degree of freedom for background suppression through algorithms like ratio-based correction and background subtraction
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 method significantly improves the visualization of targeted nerve tissues by effectively suppressing background tissues, reducing the risk of iatrogenic nerve damage and enhancing surgical precision through high-fidelity fat-suppressed images.
Implementation Method 1
a targeted nerve imaging agent that selectively binds to myelin basic protein
Implementation Method 2
one or more illumination sources configured to produce excitation light that induces fluorescence emissions in an imaging agent
Implementation Method 3
at least one beam splitter configured to separate the fluorescence emissions into a first spectral region and a second spectral region
Data Source
AI summary
Systems and methods for imaging are presented. The method includes producing excitation light configured to induce fluorescence in an imaging agent that selectively binds to a target species in a region of interest (ROI) of a subject that also includes a background species. A first and a second spectral region are selected such that a determined difference between fluorescence corresponding to the target and the background species in the first spectral region differs from a corresponding difference in the second spectral region. First and second fluorescence images are generated from the fluorescence corresponding to the first and second spectral regions. Additionally, a fluorescence ratio for the background species in the first and second fluorescence image is determined. The first fluorescence image is then multiplied or divided with the determined ratio to generate an intermediate image that is subtracted from the second fluorescent image to reconstruct a background-subtracted image.