Workflow and hardware for intraoperative hyperspectral data acquisition in neurosurgery

Abstract

To prevent further brain tumour growth, malignant tissue should be removed as completely as possible in neurosurgical operations. Therefore, differentiation between tumour and brain tissue as well as detecting functional areas is very important. Hyperspectral imaging (HSI) can be used to get spatial information about brain tissue types and characteristics in a quasi-continuous reflection spectrum. In this paper, workflow and some aspects of an adapted hardware system for intraoperative hyperspectral data acquisition in neurosurgery are discussed. By comparing an intraoperative with a laboratory setup, the influences of the surgical microscope are made visible through the differences in illumination and a pixel- and wavelength-specific signal-to-noise ratio (SNR) calculation. Due to the significant differences in shape and wavelength-dependent intensity of light sources, it can be shown which kind of illumination is most suitable for the setups. Spectra between 550 and 1,000 nm are characterized of at least 40 dB SNR in laboratory and 25 dB in intraoperative setup in an area of the image relevant for evaluation. A first validation of the intraoperative hyperspectral imaging hardware setup shows that all system parts and intraoperatively recorded data can be evaluated. Exemplarily, a classification map was generated that allows visualization of measured properties of raw data. The results reveal that it is possible and beneficial to use HSI for wavelength-related intraoperative data acquisition in neurosurgery. There are still technical facts to optimize for raw data detection prior to adapting image processing algorithms to specify tissue quality and function.

Comparison of different spectral cameras for image-guided organ transplantation

“…However, the low SNR of the first data points of the hyperspectral pushbroom as well as the multispectral -VIS camera are caused by the low illumination intensity in that spectral interval due to the quartz-tungsten-halogen spectrum. 10 , 14 , 29 …”

“…To obtain the reflectance spectrum from the measured raw data, each image has to be corrected according to where is the resulting reflectance spectrum, is the measured raw data, contains the dark reference data, and is the white reference intensity spectrum, as described in particular by Mühle et al. 14 and Wisotzky et al. 10 , 29 …”

“… 9 For different biomedical areas, e.g., histology, wound healing, retinal diseases, or cancer detection, the potential of HMSI has been shown. 9 , 11 – 14 It shows the feasibility of detecting variations in Hb at different oxygen saturation levels 14 – 17 of mapping water content in organs 18 as well as to evaluate organ blood flow characteristics. 19 , 20…”

“…9 For different biomedical areas, e.g., histology, wound healing, retinal diseases, or cancer detection, the potential of HMSI has been shown. 9,[11][12][13][14] It shows the feasibility of detecting variations in Hb at different oxygen saturation levels [14][15][16][17] of mapping water content in organs 18 as well as to evaluate organ blood flow characteristics. 19,20 In organ preservation prior to transplantation, HMSI could be a potential tool to continuously monitor several functional parameters of the organ simultaneously and non-invasively.…”

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation

“…Yet, these are prone to produce motion artefacts due to insufficient imaging speed for a dynamic scene during surgery. More recently, two intraoperative systems based on pushbroom HSI cameras were presented that allow for integration into the surgical workflow: In Mühle et al ( 2021 ), the TIVITA system (Kulcke et al 2018 ) was attached to a surgical microscope to capture in vivo neurosurgery data; in Köhler et al ( 2020 ), a laparoscopic HSI camera was presented and tested using resected esophagus tissue and in Hu et al ( 2020 ) a HSI imaging system was tested during liver cancer surgery. While these systems show potential for seamless integration into the surgical workflow, their restricted imaging speed is likely to remain an inhibitor for adoption during surgery.…”