A deep-learning-based high-resolution sound-speed reconstruction method for USCT that employs traveltime and reflection tomography images: applications to clinical data

Ultrasound computed tomography (USCT) aims to reconstruct high-resolution maps of acoustic tissue properties, such as speed-of-sound (SOS). Previous work proposed a learned reconstruction method using paired traveltime (TT) and reflection tomography (RT) images as inputs for efficient high-resolution SOS estimation. This study extends the previous virtual imaging work to real-world validation using clinical data. A convolutional neural network was trained on clinical data, using SOS maps from full-waveform inversion as targets. The experiments demonstrated the dual-input-modality IILR method's effectiveness at providing high-resolution and accurate SOS estimates, with a dramatically reduced computational burden compared to FWI. The method also demonstrated the ability to mitigate certain artifacts that were present in the FWI target images. This work suggests the promise of the learned reconstruction method for enabling clinical applications of breast USCT in low resource settings.