75124Hypervision SurgicalClosedStudyUKRI Inn.ScholarPhysical illnesses of the brain, including brain cancers and blood vessel abnormalities, have a significant and devastating impact on the lives of many UK citizens. Neurosurgical procedures play a vital role in treatment for many cases. Each year in the UK, over 70,000 patients are diagnosed with a brain tumour, of which 5,000 undergo surgery. A further 1,000 patients undergo blood vessel surgery. However, despite the importance of surgical intervention as a mainstay of treatment, many of these procedures lack surgical precision and suffer from complications, with tragic outcomes for both patients and healthcare services. There is an acute need to improve surgical outcomes for affected brain tumour patients. Crucially for these patients, significantly improved outcomes and a corresponding increased life expectancy do occur in cases where complete tumour removal is achieved. However, in almost 30% of cases this is not achieved, and patients are left with residual tumour tissue after surgery. Successful surgery mandates maximal _safe_ tumour removal: surgeons need to avoid damaging sensitive areas that undertake vital functions and preserve crucial nerves and blood vessels. Unfortunately, even with the most advanced current techniques, it is not possible to reliably identify tumour and critical structures during surgery. Advanced optical imaging techniques provide a promising solution for computer-assisted tissue recognition and understanding of blood supply and oxygenation levels during surgery. Optical methods are non-contact, non-invasive, do not require x-rays, and do not require patients to be given medicines. Hyperspectral imaging (HSI) is one such optical imaging technique that exploits the ability to split light into multiple narrow colour bands far beyond the conventional red/green/blue. It enables the acquisition of much richer information than can be seen with the naked eye and can provide crucial, but currently invisible, information about critical biological structures during surgery. However, HSI data is complex and requires computer-assisted analysis to be interpretable and of use to surgeons. In this secondment, we will progress computer-assisted analysis of HSI data to achieve real-time tissue recognition and oxygenation level estimation during brain surgery. We will develop advanced light-tissue interaction models to enable automated surgical guidance as part of an intraoperative HSI medical device that builds on novel state-of-the-art imaging hardware to guide clinicians during neurosurgical procedures. Together with a team of scientists, engineers, and neurosurgeons, we will demonstrate the clinical benefit our device offers for individual patients and the cost-savings opportunities for healthcare services.