Intraoperative hyperspectral imaging for neurosurgery: Surgical workflow optimisation and validation

Project reference: SIE_02_21
First supervisor: Jonathan Shapey 
Second supervisor: Tom Vercauteren

Start date: October 2021

Project summary:  Patients undergoing brain tumour surgery have significantly improved outcomes and increased life expectancy if complete tumour removal is achieved. Successful surgery mandates maximal safe tumour removal but even with the most advanced current techniques, it is not possible to always reliably identify tumour and critical structures during surgery. 

Hyperspectral imaging (HSI) is an advanced optical imaging technique that provides a promising solution for real-time computer-assisted tissue recognition during surgery by the safe application of light alone. HSI exploits the ability to split light into multiple narrow colour bands and can provide crucial but currently invisible information about critical biological structures during surgery.

This project will evaluate and translate a novel compact intraoperative HSI device in patients undergoing brain tumour surgery. The student will optimise the device for surgical use and will perform a clinical patient study assessing its integration into the neurosurgical workflow and quantitative image analysis.

 

Project description: Difficult intraoperative decisions with potentially life-changing consequences for patients are still based on the surgeon’s subjective visual assessment. Successful surgery mandates maximal safe tumour removal but even with the most advanced techniques available, it is nonetheless not possible to reliably identify tissue boundaries intraoperatively.

Advanced optical imaging techniques provide a promising solution for intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. By splitting light into multiple narrow spectral bands far beyond what the naked eye can see, hyperspectral imaging (HSI) has the potential to provide rich tissue-differentiation information over the entire field of view used by neurosurgeons. Previously presented intraoperative HSI (iHSI) systems demonstrated the potential to provide intraoperative tissue characterisation but the developed hardware was large, slow and cumbersome, restricting its practical use.

In previous work, we established and validated key design specifications for an iHSI system. We concluded that clinical translation would be facilitated by developing a standalone light-weight device independent of the operating microscope by integrating an HSI sensor with either a neuro-endoscope or a surgical “exoscope”: a compact high-definition video telescope operating system recently proposed as an alternative to the operating microscope.

 

Aim: This project will evaluate and contribute to the optimisation of a pre-CE-mark and pre-commercial HSI medical device for intraoperative surgical guidance in brain tumour surgery. 

To deliver on this primary objective, the project will pursue the following aims:

(A1) Investigate and optimise surgical integration of the intraoperative HSI (iHSI) device into the clinical workflow

(A2) Investigate correlation between in-vivo HSI and ex-vivo histological analysis of corresponding biopsied pathological tissue

(A3) Investigate correlation between in-vivo HSI and the Magnetic Resonance Imaging (MRI) appearance of imaged tissue

(A4) Investigate the accuracy of the iHSI device to differentiate tumour, normal tissue, nerves and blood vessels from in-vivo iHSI data

(A5) Disseminate the study’s outcomes and the device’s potential to patients and the public

(A6) Knowledge exchange with the project’s external partner (Hypervision Surgical Ltd)

 

Methodology

Year 1: The student will evaluate the ability to integrate a novel compact intraoperative HSI device into the neurosurgical workflow using state-of-the-art simulation facilities including a fully-equipped operating suite.  Results from the simulation study will inform device design modifications, in preparation for the in-vivo clinical study (A1, A6). In Year 1, the student will receive appropriate skills training (e.g. GCP training) to enable them to conduct a clinical study.

 

Year 2 and 3: The student will conduct a clinical study evaluating the device in 63 patients undergoing brain tumour surgery and 18 patients undergoing neurovascular surgery (Application for ethical approval in process). 

HSI data points will be acquired and correlated with intraoperative video recordings, registered preoperative MRI, navigated neurostimulation and neuropathological analysis of biopsies (tumour surgery only) and postoperative imaging (A2, A3). The labelled dataset of pathological tissue and normal brain tissue will be used to train and test data-driven machine learning algorithms for tissue differentiation (A4). 

Research results will be disseminated through publications in high-impact journals, presentations at leading conferences and public engagement events (A5).

 

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