Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468 − 787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.

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.

No abstract