Hyperspectral Imaging Virtual Hypercube Demosaicking
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Solution Overview
Problem
Current hyperspectral imaging systems for medical applications are unable to provide real-time, wide-field, and high-resolution tissue characterisation necessary for effective surgical guidance, due to limitations in imaging speed and resolution.
Innovation Solution
A method and system that enhance hyperspectral imaging by using spatiospectral-aware demosaicking processes to build virtual hypercubes from lower resolution imagery, allowing for the estimation of high-resolution parameters and tissue properties in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If snapshot hyperspectral cameras are used for real-time imaging, then imaging speed is improved, but spatial resolution deteriorates
Solution Approach 1:
A virtual hypercube is introduced as an intermediary data structure that bridges the gap between low-resolution snapshot mosaic images and high-resolution hyperspectral imagery. The virtual hypercube contains estimated high-resolution spectral signatures for each pixel, enabling subsequent high-resolution parameter estimation without requiring full high-resolution hyperspectral data acquisition
Solution Approach 2:
The system performs preliminary demosaicking and virtual hypercube construction from the snapshot mosaic data before parameter estimation. This preliminary processing prepares the data in a format that enables high-resolution parameter extraction while maintaining the speed benefits of snapshot imaging
2Productivity
If snapshot hyperspectral cameras are used for real-time imaging, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system changes the parameter representation from raw pixel values to spectral signatures and then to tissue parameters. By transforming the data through the virtual hypercube intermediate representation, the system extracts high-resolution tissue parameters (such as oxygen saturation, blood volume, etc.) that are more clinically relevant than raw spatial resolution
Solution Approach 2:
The patent replaces the mechanical/optical system limitation (snapshot camera resolution) with a computational approach. Instead of relying on optical resolution, the system uses computational demosaicking and spectral analysis to achieve high-resolution tissue characterisation from lower-resolution input data
Data Source
AI summary
Methods and systems for determining parameters of a desired target image from hyperspectral imagery obtained from a surgical procedure are described herein. Techniques may include capturing in real time hyperspectral snapshot mosaic images of a scene using a hyperspectral image sensor coupled to an optical scope, the snapshot mosaic images being of relatively low spatial and low spectral resolution; undertaking spatio-spectrally aware demosaicking of the snapshot mosaic images, the demosaicking comprising upsampling of the snapshot mosaic images and the application of a spectral calibration operator, to generate a virtual hypercube of the snapshot mosaic image data, the virtual hypercube comprising image data of relatively high spatial resolution compared to the snapshot mosaic images; from the image data in the virtual hypercube, determining relatively high spatial resolution parameters of a desired target image; and outputting in real time the determined relatively high-resolution parameters as representative of the desired target image.