Many functional imaging modalities such as EEG, fMRI, and fUS were developed, and have the potential to achieve the brain-computer interface. In addition to those conventional functional imaging modalities which measure the electrical signal or neurovascular coupling induced by brain activity, Patz et al. found evidence for the existence of a neuromechanical coupling that can be detected by elastography. This project proposes transcranial functional shear wave elastography (SWE) that images the elasticity of the human brain tissue with ultrasound noninvasively. To correct aberration induced by the skull, we introduce an aberration correction algorithm based on ray tracing which corrects the aberration for both shear wave generation and image readout. The result shows that the aberration correction can improve the SNR and contrast of shear wave images and help the reconstruction of elasticity of agar phantom which includes an aberrator and help SWE recover the elasticity of brain tissue through the aberrator such as skull. Although transcranial functional SWE still has weakness in spatial range of measurements, temporal resolution, and spatial resolution, it has the potential to be applied to BCI as the development of shear wave tracking technique and high frame rate ultrasound imaging.

Ultrafast ultrasound localization microscopy (ULM) is a super-resolved vascular imaging method that provides a 10-fold improvement in resolution compared to ultrafast ultrasound Doppler imaging. Because typical ULM acquisitions accumulate large numbers of synthetic microbubble (MB) tracks over hundreds of cardiac cycles, transient hemodynamic variations such as pulsatility get averaged out. Here we introduce two independent processing methods to retrieve pulsatile flow information from MB tracks sampled at kilohertz framerates and demonstrate their potential on a simulated dataset. Our first approach filters out ULM localization grid artifacts and successfully recovers the pulsatility fraction Pf with a root mean square error of 3.3%. Our second approach relies on the derivation of the velocity distribution of MBs as observed from a stationary observer. We show that pulsatile flow gives rise to a bimodal velocity distribution with peaks indicating the maximum and minimum velocity of the cardiac cycle. Measuring the locations of these peaks, we successfully estimated Pf with an error of 5.2%. Last, we evaluated the impact of the MB localization precision σ on our ability to retrieve the bimodal signature of a pulsatile flow. Together, our results demonstrate that pulsatility can be retrieved from high framerate ULM acquisitions and that the estimation of the pulsatility fraction improves with MB localization precision.