  Ultrasound computed tomography (USCT) is an emerging imaging modality that
holds great promise for breast imaging. Full-waveform inversion (FWI)-based
image reconstruction methods incorporate accurate wave physics to produce high
spatial resolution quantitative images of speed of sound or other acoustic
properties of the breast tissues from USCT measurement data. However, the high
computational cost of FWI reconstruction represents a significant burden for
its widespread application in a clinical setting. The research reported here
investigates the use of a convolutional neural network (CNN) to learn a mapping
from USCT waveform data to speed of sound estimates. The CNN was trained using
a supervised approach with a task-informed loss function aiming at preserving
features of the image that are relevant to the detection of lesions. A large
set of anatomically and physiologically realistic numerical breast phantoms
(NBPs) and corresponding simulated USCT measurements was employed during
training. Once trained, the CNN can perform real-time FWI image reconstruction
from USCT waveform data. The performance of the proposed method was assessed
and compared against FWI using a hold-out sample of 41 NBPs and corresponding
USCT data. Accuracy was measured using relative mean square error (RMSE),
structural self-similarity index measure (SSIM), and lesion detection
performance (DICE score). This numerical experiment demonstrates that a
supervised learning model can achieve accuracy comparable to FWI in terms of
RMSE and SSIM, and better performance in terms of task performance, while
significantly reducing computational time.
