Background:
Sound speed imaging is an important characteristic of ultrasound computed tomography (USCT). Full waveform inversion (FWI) is regarded as the most promising algorithm for high-resolution sound speed imaging. However, FWI may encounter the phenomenon of cycle-skipping, which means falling into local optimal solutions. Implicit neural representation (INR) is a popular technique...
Ultrasound computed tomography (USCT) has the potential for clinical applications due to its standardized operations and multi-modality. However, obtaining high-quality images requires a complete dataset including all transmitting-receiving pairs, resulting in a time-consuming scanning process and substantial data-processing demands. The limitation restricts the clinical applications of USCT....
Transcranial ultrasound computed tomography using Full-Waveform Inversion presents a unique challenge due to the non-linear physics and the computational expense of wave physics. We address this challenge with a probabilistic framework that learns to sample the Bayesian posterior of brain parameters that match the data. To scale to realistic parameter sizes, we use normalizing flows and...
