Labeled numerical phantom of abdominal wall for wave-physics based ultrasound imaging: applications to image reconstruction and parameter estimation (Part B)

The abdominal wall consists of an acoustically complex organization of tissue layers that generate significant degradation in diagnostic ultrasound imaging of internal organs. As with any non-invasive diagnostic medical imaging modality, the underlying ground truth is a fundamentally unknown quantity in a living patient. A realistic labeled 3D numerical phantom of the human abdominal wall is used to establish the acoustical properties of human tissue. Simulations based on the first principles of wave propagation, including the effects of propagation, aberration, reflection, refraction, attenuation, scattering, reverberation, and nonlinearity are used to generate a large data set of raw ultrasound data for 2D imaging. This data set is then used to train a physics-informed neural network to generate local sound speed estimates. Finally the translation of this approach is demonstrated in vivo on clinical data of liver ultrasound images. It is shown that together, the neural network and this training data set generate high quality estimates of the sound speed as measured by improvements in clinical image quality. The labeled abdominal data set, the simulation tools that model wave propagation, and the neural network approach are made publicly available. Other training and optimization approaches can be applied to this data.