About the Project

Cerebral blood flow is a critical indicator of brain health. Sufficient blood flow supports essential brain functions, maintains proper metabolism, and enables adaptive responses to external stimuli. Disruptions caused by conditions such as stroke or traumatic brain injury can severely impair brain function and endanger patient outcomes. Nonetheless, continuous and noninvasive monitoring of cerebral blood flow at the bedside remains challenging. Current methods fall short of meeting this need: MRI requires transporting patients out of the ICU, risking destabilization; CT, while sometimes available on-site, uses ionizing radiation and is limited to intermittent measurements that might miss critical therapeutic windows; and transcranial ultrasound, although portable, is not designed for continuous monitoring and primarily assesses blood flow in large vessels rather than in the microvasculature. These limitations highlight the urgent need for advanced, affordable, and accessible technologies in neurocritical care.

The goal of this project is to develop and validate a pioneering, noninvasive, portable cerebral blood flow monitor for bedside use in neurocritical care. This innovative device will utilize speckle-based diffuse optical techniques—specifically, diffuse correlation spectroscopy (DCS) and speckle contrast optical spectroscopy (SCOS)—to capture real-time data on cerebral blood flow. The project will integrate cutting-edge hardware and software for data collection with machine learning algorithms for advanced data analysis. Ultimately, the successful candidate will deliver a robust device capable of sophisticated data collection and intelligent analysis, paving the way for its translation into clinical practice.

The project will be based in the Medical Imaging Lab at the University of Birmingham's School of Computer Science, which has facilities for instrumentation development and data analysis. We are seeking a motivated candidate with a background in engineering, physics, or computer science. Ideal candidates will have previous experience in electronics and/or systems control (or a willingness to learn these topics) and a passion for combining modelling and programming in biomedical engineering. Additional skills that will be beneficial include programming, mathematical proficiency, effective communication, and the ability to work collaboratively within a multidisciplinary team. The successful applicant will work within a supportive and dynamic interdisciplinary team specialised in optical neuroimaging methods. They will receive close supervision and have opportunities to interact and collaborate with researchers from other fields.