Context and relevance for biomedical diagnostics and molecular research
The precise analysis of biological processes in real time is a central goal of modern biomedicine. Functional and molecular information play an increasingly important role, especially in diagnostics and personalized therapy. Traditional imaging methods often reach their limits: they primarily provide structural data, frequently rely on contrast agents, and allow only limited dynamic interpretation of pathological processes.
Hyperspectral imaging fundamentally changes this paradigm. Instead of single image data, a complete spectrum per pixel is generated—providing a molecular fingerprint of the tissue under examination. This spectral depth opens up new possibilities for identifying disease processes before visible morphological changes occur. Significant added value arises, particularly in early diagnostics, intraoperative decision support, and the development of new drugs and therapeutic procedures, benefiting both research and clinical application.
Requirements from clinical and scientific perspectives
For the successful adoption of hyperspectral imaging in the biomedical field, several requirements must be met. Crucially, the ability to analyze tissue nondestructively and without contrast agents in real time is essential. Equally important is high imaging stability without moving parts to avoid artifacts caused by mechanical movement and to enable integration into sensitive laboratory environments.
Additionally, the technology must seamlessly integrate into existing platforms such as microscopes, endoscopes, or surgical systems without compromising their functionality. Interoperable connectivity with laboratory automation, digital workflows, and AI-powered evaluation systems is necessary to efficiently utilize the vast amounts of data and ensure reproducible results. Cubert’s snapshot technology meets all these requirements by simultaneously capturing all spectral bands of an image—without mechanical scanning, with validated real-time processing, and an open interface architecture.
Application areas in research and clinical practice
In neurodegenerative research, hyperspectral imaging offers new possibilities to detect pathological changes at very early stages. Spectral deviations in oxygen supply, protein expression, or tissue composition can be distinguished even before conventional methods reveal noticeable findings. This enables a deeper understanding of disease mechanisms and improves the chances for early therapeutic interventions.
The technology is also increasingly used in surgical oncology. During a procedure, the system provides real-time information about the molecular composition of the tissue. This allows reliable identification of tumor margins and maximal preservation of healthy tissue—a crucial advantage in surgeries involving the brain, abdominal cavity, or urogenital area.
In drug development, spectral analysis enables automated assessment of cellular responses to various substances. Changes in cell metabolism, membrane integrity, or differentiation states can be clearly assigned at the spectral level. This accelerates high-throughput processes, refines the investigation of mechanisms of action, and facilitates early identification of toxicological risks.
Furthermore, in regenerative medicine, the technology opens new diagnostic avenues for monitoring healing processes or differentiating stem cells. Processes previously accessible only through complex histology or immunological tests can now be visualized live and with spectral depth.
Integration potential into biomedical platforms
Hyperspectral technology can be flexibly and modularly integrated into existing systems. In microscopy, it can be used as an add-on module to spectrally analyze cell structures and biological samples at the nanoscale. In minimally invasive surgery, it is increasingly integrated into endoscopes to directly detect pathological areas during procedures.
In cell culture and incubation, the technology enables continuous, non-invasive analysis of the biological environment, allowing natural growth to remain undisturbed while monitoring changes without sampling. In operating rooms, the system can be adapted to existing lighting units or robotic platforms, providing real-time operative guidance.
Research and technology insights
Hyperspectral imaging offers new momentum for developing innovative analytical methods in scientific institutions. The analysis of spectral data using artificial intelligence enables automatic classification of cell types, tissue states, or disease stages. Trained models recognize characteristic signatures even within complex biological noise.
Combining hyperspectral data with other modalities—such as MRI, PET, or ultrasound—creates multimodal systems that provide a more comprehensive picture of pathological processes. Additionally, validating new molecular markers through spectral signatures opens new avenues in biomarker research.
Finally, edge computing technologies allow direct preprocessing and analysis on the sensor platform. This makes hyperspectral diagnostics usable outside the lab or in mobile telemedicine scenarios—a crucial factor for healthcare delivery in underserved regions or time-critical emergencies.
Technology status and system availability
The Cubert snapshot technology is already in use in numerous translational projects and clinical studies. It is employed by university hospitals in Germany, Fraunhofer institutes, Helmholtz centers, as well as in the pharmaceutical industry. The systems are CE-certified, ITAR-free, and equipped with open interfaces, enabling seamless integration into existing workflows.
Their stability, miniaturization, and real-time capability make them a versatile tool in biomedical research, diagnostics, and therapy development.
Data sovereignty, interoperability, and scientific autonomy
A key advantage of Cubert technology is complete data sovereignty. All acquisition, processing, and analysis processes remain with the system operator. There is no cloud connection, no dependency on third-party providers, and no limitations imposed by licensing models. This enables research institutions to build their own spectral libraries, train AI models, and independently develop their infrastructure. This not only creates technological sovereignty but also legal security for sensitive medical and scientific data.
About the Author
Dr. Matthias Locherer has been the Sales Director at Cubert GmbH since 2017. With a PhD in Earth Observation from Ludwig Maximilian University of Munich, he brings extensive expertise in remote sensing, spectral imaging, and data analysis. Matthias has contributed to numerous research projects and publications, particularly in the hyperspectral monitoring of biophysical and biochemical parameters using hyperspectral satellite missions. His deep knowledge of optical measurement techniques and physical modeling makes him a key driver in advancing innovative hyperspectral technologies at Cubert.
