Current Awardee

Toward Ultrasound Brain Imaging via Full-Wave Acoustic Simulations and Deep Learning

Aiguo Han
Aiguo Han

College: Grainger College of Engineering
Award year: 2021-2022
NCSA collaborators: Volodymyr Kindratenko

Transcranial ultrasound could enable a broad variety of applications in imaging, including functional brain imaging, intracerebral hemorrhage detection, brain perfusion evaluation, and cerebrovascular disease (e.g., stroke) diagnosis, among others. Despite the great potentials, transcranial ultrasound brain imaging has not been widely used in adults. This is in large part because adult human skulls cause severe phase aberration, leading to highly degraded ultrasound images. The research team proposes a real-time pulse-echo ultrasound approach to estimate the skull profile and speed of sound using deep learning methods with ultrasound radiofrequency signals backscattered from the skull. The hypothesis is that with sufficient training, deep learning is capable of extracting the skull profile and speed of sound from radiofrequency signals, and deep learning-extracted skull profile and speed of sound allow accurate skull aberration correction for transcranial ultrasound imaging. The objective of this project is to establish the feasibility of the proposed methods.