  Ultrasound computed tomography (USCT), as an emerging technology, can provide
multiple quantitative parametric images of human tissue, such as sound speed
and attenuation images, distinguishing it from conventional B-mode (reflection)
ultrasound imaging. Full waveform inversion (FWI) is acknowledged as a
technique with the greatest potential for reconstructing high-resolution sound
speed images in USCT. However, traditional FWI for sound speed image
reconstruction suffers from high sensitivity to the initial model caused by its
strong non-convex nonlinearity, resulting in poor performance when ultrasound
signals are at high frequencies. This limitation significantly restricts the
application of FWI in the USCT imaging field. In this paper, we propose an
untrained neural network (UNN) that can be integrated into the traditional
iteration-based FWI framework as an implicit regularization prior. This
integration allows for seamless deployment as a plug-and-play module within
existing FWI algorithms or their variants. Notably, the proposed UNN method can
be trained in an unsupervised fashion, a vital aspect in medical imaging where
ground truth data is often unavailable. Evaluations of the numerical simulation
and phantom experiment of the breast demonstrate that the proposed UNN improves
the robustness of image reconstruction, reduces image artifacts, and achieves
great image contrast. To the best of our knowledge, this study represents the
first attempt to propose an implicit UNN for FWI in reconstructing sound speed
images for USCT.
