Imaging methods are used to obtain visual representations of objects that are otherwise invisible to the naked eye. The physical principles in which imaging methods are based are common across disciplines and, hence, can be adapted. Here I propose to lead an inter-disciplinary project that will focus on obtaining images of medical and geophysical targets that are traditionally difficult to image with ultrasound or seismic waves, such as the brain.

Rapid brain imaging is central to the diagnosis and treatment of stroke and other acute neurological conditions, but existing methods for imaging the brain (mainly X-rays and magnetic resonance imaging) require large, immobile, high-power instruments that are near-impossible to deploy outside specialised environments. I will create a device that can be applied to any patient, at any time and in any place by exploiting advances that have already revolutionised imaging in geophysics and using ultrasound waves transmitted across the head. In particular, I will adapt an imaging algorithm known as full-waveform inversion to transform the recorded ultrasound data into the first highly detailed image of an adult brain with ultrasound, and with a much higher resolution than those obtained with conventional ultrasound. To achieve this goal, I will design a safe and suitable device for its application to healthy volunteers, and I will use the recorded data and full-waveform inversion conveniently adapted. This will require solving several technical aspects, such as accounting for involuntary movement due to breathing, obtaining the characteristics of the skull from the data and accelerating the computations on graphics processing units. The success of this project would represent a major breakthrough in brain imaging and would be particularly relevant to improve the survival rate and wellbeing of patients with acute stroke, which is the second-largest cause of death and acquired adult disability.

Then, I will study the capability of ultrasound full-waveform inversion for breast cancer detection, in particular for patients with dense breasts in which traditional mammography fails, and for bone imaging - in particular for detecting osteoporosis and fractures. To achieve these goals, I will develop and validate in the laboratory new full-waveform inversion algorithms to recover multiple characteristics of biological tissues and I will use low-frequency ultrasound that easily penetrates bone.

Next, I will investigate the potential of full-waveform inversion of ultrahigh-frequency seismic data, a particular type of seismic waves that travel small distances but can interact with small objects, in order to characterise the first 100 meters of the subsurface in offshore wind farms. This new approach will be particularly useful to characterise vast areas of the subsurface and locate adequate regions for the installation of wind turbines to reduce maintenance costs.

Finally, I will evaluate different strategies to obtain subsurface images over time with full-waveform inversion of seismic data at carbon dioxide storage sites, which play a crucial role in reducing the carbon footprint. This will help engineers better understand how carbon dioxide reservoirs evolve and how to make them safer and more efficient.