New experimental and computational methods for ultrasound brain tomography

Fast, portable, and affordable neuroimaging is currently unavailable in clinical practice, hindering prevention and treatment of pathologies such as stroke, a leading cause of death and disability worldwide. Full-waveform inversion (FWI), an ultrasound-based tomographic technique, has been recently proposed as a solution to this problem, but is yet to be successfully applied experimentally. Two fundamental barriers hinder its experimental application: a lack of numerical models that accurately replicate experimental measurements, and of domain-specific software that implements FWI algorithms efficiently. Here, I address both problems, opening the door to universally available neuroimaging. Addressing the first barrier entails finding numerical models that can explain the behaviour of the acquisition system: the transmission and reception response of the transducers, and their spatial location and orientation. As I demonstrate here, existing position-estimation methods fail when the surface of the transducers is bigger than the wavelength, a prerequisite for imaging through the skull, while available response-estimation techniques cannot achieve the precision required by full-wave methods. Therefore, I present spatial response identification, a new algorithm for transducer calibration and modelling, and show how it can be used to explain experimental devices with higher accuracy than existing methods. Additionally, I present experimental reconstructions of a tissue-mimicking phantom, achieving improved imaging quality with respect to standard calibration techniques. The second barrier stems from the fact that FWI is mathematically challenging and orders of magnitude more computationally expensive than conventional ultrasound imaging, while there is an absence of open codes, slowing the pace of research and hindering reproducibility. Therefore, I introduce Stride, an open-source Python library that combines high-level interfaces with automatically generated, high-performance solvers and scalable parallelisation. Here, I demonstrate that Stride can achieve state-of-the-art modelling accuracy and how it can be used to image in 2D and 3D, scaling from a local workstation to a high-performance cluster.Open Acces