HYPERSPECTRAL IMAGING APPLICATIONS IN NEUROSURGERY

Only a handful of HSI systems have been reported for neurosurgery, and the research focuses on brain tumor surgery. This section describes the applied system types and provides an overview of neurosurgical HSI indications.

2.4.1 Neurosurgical hyperspectral imaging systems

The HSI systems reported in neurosurgical literature are either standalone or mounted to the operating microscopes [148,149,203,214,220]. The systems comprise hyperspectral cameras, light sources, and mounting devices, such as adapters or tripods [148,214]. In addition, some systems may include additional devices, such as lenses, optic fibers, or orientation- measuring devices [203].

The HELICoiD demonstrator is the first and the most well-described neurosurgical HSI system. The system is an unsterile standalone tower measuring 2000 x 600 x 1100 𝑚𝑚 , and it comprises two hyperspectral cameras, a halogen light source, a scanning platform, and stepper motors [214]. It acquires HSI data in the 400-1000 nm range with an imaging time of approximately 60 seconds. Repeatability measurements have been reported for the system [191], and the group has tested several machine learning algorithms designed for glioma surgery [195,208]. More compact intraoperative HSI systems have been reported since the HELICoiD project.

Urbanos et al. used a standalone snapshot HSI system comprising a supporting stand, a halogen light source, and a compact HSI camera measuring 26 x 26 x 26 mm [203]. The system was designed for brain tumor surgery and recorded 25 bands in the range of 655-975 nm in a few milliseconds.

As opposed to standalone systems, microscope-mounted systems allow spontaneous imaging and integrate into the operating workflow fluently (Figure 20). Mühle et al. developed a line-scan HSI system using a halogen light source, custom adapters, and a line-scan hyperspectral camera integrated to an S100 OPMI Pico surgical microscope [149]. Hyperspectral

cubes were captured in the range of 500-1000 nm with an acquisition time of 50 seconds. The system did not limit the microscope’s mobility, controls, or balancing, and imaging could be done spontaneously during the

operation. As opposed to the HELICoiD demonstrator, the system was covered with a sterile drape, as is done routinely. The authors conducted hardware and illumination validation both in the laboratory and clinical settings to improve the system’s signal-to-noise ratio. The system was applied to clinical practice in a single operation for HGG [149].

Figure 20. Our hyperspectral imaging system integrated to the operating microscope. Primary oculars and the controls of the operating microscope (A), hyperspectral camera (B), sterile drape (C), and a portable workstation to control the hyperspectral image acquisition (D).

2.4.2 Neuro-oncology

HGG surgery is the most reported application of neurosurgical HSI

[149,195,203]. As the first in the field, the collaborative European HELICoiD project demonstrated that HSI could automatically classify healthy cortex, brain tumor, and blood vessels in a cross-validated case series [191,214].

The authors collected a total of 36 hyperspectral images from 22 patients from two different hospitals. Over 300 000 pixels’ spectral signatures were labeled for analysis using different machine learning methods. From the same group, Manni et al. reported a deep learning model which classified 12 hyperspectral images from nine patients with HGG [208]. The

convolutional neural network algorithm classified normal tissue, tumor tissue, blood vessels, and background with an overall accuracy of 80%, outperforming the simpler machine learning algorithms tested previously.

Sensitivity and specificity for tumor tissue were 68% and 98%, respectively.

The authors later reported the optimal wavelength bands to identify glioma tissue, comparisons of different spectral cameras and machine learning algorithms, along with technical improvements to the imaging system [171,221,222].

Urbanos et al. collected 13 hyperspectral images of 12 patients with HGG [203]. Healthy cortex, veins, arteries, tumor, and dura mater were classified using SVM, random forest (RF), and three-dimensional neural network algorithms. Overall accuracies of 60.1%, 52.9%, and 49.1% were achieved, and the sensitivities for tumor class were 20%, 11%, and 32%, respectively. The SVM algorithm had the highest overall accuracy per band of 2.4%, improving the efficiency of the single band compared to the previous benchmark of 0.63% by Manni et al. [203,208].

In addition to the two-case series, case reports of HGG HSI analysis using a RF classifier [149] and PCA [220] have been reported.

2.4.3 Neurovascular

Along with brain tumor surgery, cortical oxygenation and vessel detection are potential applications for intraoperative HSI. In 2021, Iwaki et al.

intraoperatively monitored brain perfusion using a line-scan HSI system in 29 patients with Moya-Moya disease [223]. Hyperspectral images were acquired in 5-16 seconds during superficial temporal artery to middle cerebral artery bypass surgery. Quantified oxygenation maps were overlaid to microscope images, and HSI could detect an increase in cortical

perfusion after anastomosis relative to those without. The findings matched with the current gold standard, postoperative single-photon emission computed tomography [223]. Furthermore, cortical oxygenation and blood flow have been evaluated using intraoperative HSI in several case reports of 1-4 patients [193,224–226]. In a proof-of-concept research by Laurence et al., the authors showed that spectral imaging could

distinguish cortex, blood vessels, and bleeding at the brain surface based on variations in hemoglobin concentration [192]. However, the findings from three patients require confirmation in a larger cohort.

2.4.4Fluorescence-guided surgery

Multispectral fluorescence imaging is the foundation of modern neurosurgical FGS systems, and these reports date to as early as 1998 [227,228]. In clinical practice, fluorescence is assessed subjectively;

however, recent HSI reports aim to quantify fluorescence and objectify decision-making [183,229–231]. Valdés et al. used a microscope-mounted hyperspectral camera to quantify PpIX fluorescence in 12 glioblastoma patients [231]. In their study, quantified PpIX concentration was overlaid intraoperatively to a microscope image, visualizing the residual tumor that was not visually detected. Similarly, Bravo et al. used a microscope-

mounted HSI system to quantify PpIX fluorescence during meningioma or astrocytoma surgery in 3 patients [183]. Both Valdes et al. and Bravo et al.

reported in situ detection level of PpIX fluorescence to be approximately 0.05-0.1 μg/ml using HSI. Concentrations down to 0.37 μg/ml were visually

detected in phantoms by neurosurgeons in contrast to concentrations down to 0.02 μg/ml detected by HSI [183,231].

Similar findings were reported from resected tumor samples and tissue phantoms [229,230]. Molina et al. analyzed 524 tumor samples from 162 patients using HSI and expert evaluation to quantify the PpIX

concentrations that could be visually detected [230]. In their study,

concentrations higher than 0.9 μg/ml could be visually discriminated, and concentrations exceeding 9.88 μg/ml were perceived as strongly

fluorescent. Similarly, Lehtonen et al. compared HSI and expert visual analysis to discriminate PpIX fluorescence in 16 tissue phantoms and eight controls [229]. In this study, the minimum PpIX concentration detection for experts was 0.6–1.8 μmol/l and 0.03–0.15 μmol/l for HSI. In all,

fluorescence quantification is a critical step in increasing the tumor resection rates and the applicability of 5-ALA FGS for other tumor pathologies outside HGGs.

2.4.5Additional indications

In addition to neuro-oncological, neurovascular, and FGS applications, HSI has been applied in spinal surgery and studied for the potential

recognition of cell families [148,232]. Using the HELICoiD demonstrator, Martínez-González et al. created a statistical model to extract binary classification maps of the presumed tumor cell families [232]. Five HGG patients were included, and in four patients, the tumor labels divided into distinct groups. As tumor cell colonies are typical for HGGs, the authors suggested that HSI could be used to identify these subgroups, but histological confirmation is warranted [232]. As another potential application, Ebner et al. used a small standalone endoscopic system to record snapshot hyperspectral images during spinal fusion surgery to identify the operated tissues [148]. This translative case report presented grayscale hyperspectral images without statistical or machine learning analysis. In all, only a few research groups have applied HSI to

neurosurgery, and there is undoubtedly potential for new indications.