Source：Shenzhen Kai Mo Rui Electronic Technology Co. LTD2026-07-15
Conventional imaging technologies rely on the visible light spectrum. As we all know, visible light is artificially divided into three primary channels: Red, Green and Blue (RGB). This classification works well for basic visual observation, yet a wealth of information carried by other wavelengths is discarded. Hyperspectral imaging makes full use of these omitted spectral data to enhance and enrich conventional visible imaging results.
Basic Principles of Hyperspectral Imaging
Hyperspectral imaging employs an imaging spectrometer to perform continuous scanning and image acquisition across dozens or even hundreds of spectral bands within the target spectral range. While capturing visual morphological features of the target object, it simultaneously acquires spectral data that reflects the chemical composition and internal properties of the specimen.
Working Mechanism？
Hyperspectral imaging is designed to capture discrete, targeted wavebands of the spectrum. This is realized either by deploying sensors sensitive to exclusive spectral segments or mounting bandpass filters in front of image sensors.The system collects data across numerous wavebands to generate a large three-dimensional data cube, whose three dimensions represent horizontal coordinates (X), vertical coordinates (Y), and spectral wavelength respectively.A grayscale image is a 2D matrix where each pixel stands for light intensity. An RGB image can be interpreted as three stacked grayscale frames, namely a 3D array with a wavelength dimension of 3. In the hyperspectral data cube, the value of every pixel corresponds to the light intensity at a specific wavelength.
Hyperspectral datasets contain far more information than standard images, bringing greater complexity to data analysis. Nevertheless, this technology requires neither contrast agents nor ionizing radiation during acquisition.
Core Advantages of Hyperspectral Imaging
Different substances feature unique reflectance signatures at varying wavelengths. Traditional RGB imaging can only distinguish material differences through color variation for visual display. By contrast, hyperspectral imaging delivers superior capability in differentiating targets within the field of view and even identifies lesions and structures invisible to the naked eye.
Blood vessels beneath tissue surfaces alter light reflectance at characteristic wavelengths, enabling non-invasive detection via hyperspectral scanning. Tumor tissues differ fundamentally from healthy tissue in biochemical properties, which leads to distinct hyperspectral reflectance curves — this property allows accurate identification and segmentation of tumor lesions. In short, hyperspectral imaging pinpoints pathological variations undetectable by human vision.
Integration of AI & Hyperspectral Imaging in Robot-Assisted Surgery
During Robot-Assisted Surgery (RAS), especially endoscopic procedures, it is critical for surgeons to avoid damaging underlying blood vessels and nerve bundles. Hyperspectral imaging enables real-time localization of these vulnerable anatomical structures.
While hyperspectral technology can detect subsurface tissue features, artificial intelligence and deep learning algorithms are introduced to achieve comprehensive visualization of hidden anatomical layers. Neural networks are trained on hyperspectral datasets to segment obscured blood vessels, nerve fascicles and tumor lesions in the operative field.
These marked structures can be overlaid directly onto conventional RGB surgical video, delivering intuitive prompts and risk alerts to surgeons. Operators may then deliberately bypass or approach target structures in line with surgical planning.
This auxiliary visualization layer improves surgical safety without exposing patients to X-ray radiation or injecting contrast media.
+8613798538021