Integrating hyperspectral imaging in an existing intra-operative environment for detection of intrinsic brain tumors

Abstract

Label-free tissue identification is the new frontier of image guided surgery. One of the most promising modalities is hyperspectral imaging (HSI). Until now, the use of HSI has, however, been limited due to the challenges of integration into the existing clinical workflow. Research to reduce the implementation effort and simplifying the clinical approval procedure is ongoing, especially for the acquisition of feasibility datasets to evaluate HSI methods for specific clinical applications. Here, we successfully demonstrate how an HSI system can interface with a clinically approved surgical microscope making use of the microscope's existing optics. We outline the HSI system adaptations, the data pre-processing methods, perform a spectral and functional system level validation and integration into the clinical workflow. Data were acquired using an imec snapscan VNIR 150 camera enabling hyperspectral measurement in 150 channels in the 470-900 nm range, assembled on a ZEISS OPMI Pentero 900 surgical microscope. The spectral range of the camera was adapted to match the intrinsic illumination of the microscope resulting in 104 channels in the range of 470-787 nm. The system's spectral performance was validated using reflectance wavelength calibration standards. We integrated the HSI system into the clinical workflow of a brain surgery, specifically for resections of low-grade gliomas (LGG). During the study, but out of scope of this paper, the acquired dataset was used to train an AI algorithm to successfully detect LGG in unseen data. Furthermore, dominant spectral channels were identified enabling the future development of a real-time surgical guidance system.