  Ultrasound computed tomography (USCT) is actively being developed to quantify
acoustic tissue properties such as the speed-of-sound (SOS). Although
full-waveform inversion (FWI) is an effective method for accurate SOS
reconstruction, it can be computationally challenging for large-scale problems.
Deep learning-based image-to-image learned reconstruction (IILR) methods are
being investigated as scalable and computationally efficient alternatives. This
study investigates the impact of the chosen input modalities on IILR methods
for high-resolution SOS reconstruction in USCT. The selected modalities are
traveltime tomography (TT) and reflection tomography (RT), which produce a
low-resolution SOS map and a reflectivity map, respectively. These modalities
have been chosen for their lower computational cost relative to FWI and their
capacity to provide complementary information: TT offers a direct -- while low
resolution -- SOS measure, while RT reveals tissue boundary information.
Systematic analyses were facilitated by employing a stylized USCT imaging
system with anatomically realistic numerical breast phantoms. Within this
testbed, a supervised convolutional neural network (CNN) was trained to map
dual-channel (TT and RT images) to a high-resolution SOS map. Moreover, the CNN
was fine-tuned using a weighted reconstruction loss that prioritized tumor
regions to address tumor underrepresentation in the training dataset. To
understand the benefits of employing dual-channel inputs, single-input CNNs
were trained separately using inputs from each modality alone (TT or RT). The
methods were assessed quantitatively using normalized root mean squared error
and structural similarity index measure for reconstruction accuracy and
receiver operating characteristic analysis to assess signal detection-based
performance measures.
