Core Concepts
Dual-channel image-to-image learned reconstruction (IILR) using traveltime and reflection tomography images as inputs effectively estimates high-resolution sound-speed distributions in ultrasound computed tomography, offering a computationally efficient alternative to full-waveform inversion.
Stats
Tumor tissues accounted for only 0.22% of the total breast tissue area within the training dataset.
The virtual imaging system used a circular measurement aperture with a radius of 110 mm and 256 transducers.
The central frequency of the source pulse was 1 MHz.
The simulated measurements were corrupted with Gaussian noise, resulting in a signal-to-noise ratio of 36 dB.
The U-Net model consisted of six blocks in both the contracting and expanding paths, with a total of 31.1 million trainable parameters.
The training dataset included 1,120 examples, the validation set had 90 examples, and the testing set also had 90 examples.