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Medical Analysis
Hyperspectral Imaging of In Vivo Brain Tissue for Surgical Guidance

Hyperspectral Brain Surgery Guidance

Surgical procedures are not without risk. In some cases, there is a risk of removing healthy tissue or failing to remove all diseased, cancerous, or unhealthy tissue. Accurate removal is crucial to patient outcomes.

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Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing

Background:

Surgical procedures are not without risk. In some cases, there is a risk of removing healthy tissue or failing to remove all diseased, cancerous, or unhealthy tissue. Accurate removal is crucial to patient outcomes.

There are multiple reasons this could occur. Blood can flood the surgical bed obstructing the physician and clinician’s view. Low visibility can also be the result of static medical images. Previous MRIs and CT scans, for example, be challenging to correlate or map onto the surgical bed during surgery. In such cases, it can be challenging to distinguish healthy from diseased tissue accurately. 

Non-invasive, highly sensitive and specific medical instrumentation can be useful in mapping and distinguishing healthy from unhealthy tissue in real-time. Hyperspectral imaging is one such tool. New research is demonstrating the clinical benefit of live mapping of healthy versus unhealthy tissue to support surgical procedures and improve patient outcomes.

Summary:

In this study, researchers demonstrate the utility of hyperspectral imaging for surgical guidance. The medical team performed an in vivo craniotomy, which is the removal of the skull to access the surgical bed. A line-scanning (or push-broom) hyperspectral imaging camera was used, operating in the VNIR spectral range from 400 to 1000 nm, on a scanning platform. The camera was operated by an engineer, positioned over the exposed brain surface. Images were captured in two instances, one before the resection of the tumor and one after. In parallel, a biopsy and a histopathological diagnosis were performed for the analysis of the type and grade of the tumor and the surrounding area.

Hyperspectral imaging provides the spectral response of each pixel of material across a range of spectral bands. In principle, different materials have unique spectral responses. Healthy tissue will reflect light differently than unhealth tissue. But these are only relative differences. The full analysis requires prediction of the absolute differences. Each pixel of material in the image must be correlated to the properties identified in the biopsy.

The team performing the study prepared a library of classification data to match the pixels of the hyperspectral image, based on their spectral response to their biochemical properties. The dataset was evaluated for accuracy, sensitivity, correlation coefficient and other specific metrics and further computed and classified in two different methods for classification.

Implications:

This proof-of-concept study demonstrated the power of hyperspectral imaging as a surgical tool. State-of-the-art hyperspectral cameras hold promise as a live mapping and measurement device. We could well imagine a live hyperspectral video feed in the near future. There are two paths for further exploration toward widespread clinical adoption. First, the camera systems must be simplified for standard clinical use. There is a need to improve the form factor, physical set-up and user interface. Second, further research is required to increase the quality of the data classification library. Hyperspectral imaging is as powerful as the reference data. More samples are needed for each application. In summary, this study shows the clinical potential of hyperspectral and provides a roadmap for improvement. Continued advancement of this technology will require collaboration between medical researches, clinicians, data scientists and optical imaging experts.

Please contact us to learn more about how we are working to advance these solutions and resolve these challenges with superior optics and data science.

Application notes