Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study

Abstract

IntroductionHyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery.MethodsA lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350–1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study.ResultsFocusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery.DiscussionThis work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.

CUVIS.AI: a new open source platform and three state-of-the-art applications of machine learning in hyperspectral imaging

“…The stability of our systems, due to the lack of moving parts within our cameras, makes the use of handheld devices possible in the surgical suite. The system will provide surgeons with unprecedented information on tissue properties that can help them achieve maximal tumor resection [5].…”

HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications

“…One fundamental problem is that the diagnosis relies on the neurosurgeon’s criteria when interpreting the results, requiring extensive prior knowledge and being susceptible to errors. For this reason, many researchers have addressed the task of automatizing the MRI interpretation by means of machine learning (ML) algorithms [ 6 , 15 , 19 ], in some cases mixing the magnetic resonance (MR) information with other medical image sources such as computed tomography (CT), positron emission tomography (PET) [ 20 ], ultrasounds (US) [ 8 ], or hyperspectral (HS) imaging (HSI) [ 9 , 14 ]. Another key challenge is the visualization, as the volumetric information generated by the MR is not easily interpretable on a 2D screen.…”