Hyperspectral Camera Probe Calibration for Tumor Margin Detection
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Solution Overview
Problem
Current methods for evaluating tumor margins during tumor resection surgery are time-consuming and prone to fluctuations due to heterogeneous tissue structures, making it challenging to efficiently and accurately identify positive margins in real-time.
Innovation Solution
A medical imaging system combining a hyperspectral camera for rapid imaging of large tissue areas with limited spectral resolution and a probe for high specificity point measurements, where the probe data calibrates the hyperspectral processing algorithm to enhance sensitivity and contrast for tumor identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a hyperspectral camera system is used to image large tissue areas, then the coverage area and imaging speed are improved, but the spectral resolution and identification specificity deteriorate
Solution Approach 1:
The system segments the tissue examination into two stages: first, a hyperspectral camera captures a broad overview of the entire tissue surface to identify suspicious regions; second, a probe with high spectral resolution is applied only to those specific suspicious areas for detailed analysis. This segmentation allows the system to maintain both wide coverage and high measurement precision without compromise.
2Measurement precision
If a probe is used to provide high specificity measurements, then the identification accuracy is improved, but the measurement area and inspection efficiency deteriorate
Solution Approach 1:
The hyperspectral camera performs preliminary scanning of the entire tissue area before the probe is applied. This preliminary action identifies which specific regions require detailed examination, allowing the high-specificity probe to be used only where necessary. As a result, inspection efficiency is maintained because the probe is not applied indiscriminately across the entire tissue surface, while identification accuracy is preserved through targeted high-resolution measurement.
3Reliability
If surface probes are used to investigate tissue margins, then the measurement stability is improved, but the time required for evaluation deteriorates
Solution Approach 1:
The system applies different measurement qualities to different regions of the tissue: a rapid, lower-resolution hyperspectral imaging quality for the entire tissue surface, and a slower, high-resolution probe measurement quality only for suspicious local areas. This local differentiation of measurement quality maintains reliability for critical margin assessment while minimizing time loss by avoiding exhaustive probing of the entire tissue surface.
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 system enables robust and efficient identification of tumor margins during surgery, reducing the need for reoperation by quickly highlighting suspicious areas for further examination with high specificity, thus improving the chances of complete tumor removal.
Implementation Method 1
a hyperspectral camera system arranged to provide a hyperspectral image covering a first surface area of the biological tissue, wherein pixels of the hyperspectral image contains information at different light wavelength bands
Data Source
AI summary
A medical imaging system for identifying a target structure (TS), e.g. a tumor, in a biological tissue. A hyperspectral camera system is used for imaging a surface area (A1) of the tissue (BT), e.g. with a limited spectral resolution, but enough to allow identification of suspicious areas where the target structure (TS) may be, e.g. such areas can be visually indicated on a display to the operator. A probe (PR), e.g. an optical surface probe, is used to provide probe measurement of a smaller surface area (A2) of the tissue, but with more information indicative of the target structure. The probe is selected to provide a higher specificity with respect to identification of the target structure than the hyperspectral camera (HSC). The hyperspectral processing algorithm (PP) is then calibrated based on probe measurement data performed within the suspicious areas, thus providing a calibrated hyperspectral processing algorithm resulting in images with an enhanced sensitivity to identify the target structure. Only few probe measurements are required to significantly improve the resulting image, thereby providing a reliable and fast target structure (TS) identification.