  Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and
penetration depth. By considering the in-vivo characteristics of human organs,
it is necessary to provide clinicians with appropriate hardware specifications
for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging
studies, including the SR task in deep learning fields, have been reported for
enhancing ultrasound images. However, most of those studies did not consider
ultrasound imaging natures, but rather they were conventional SR techniques
based on downsampling of ultrasound images. In this study, we propose a novel
deep learning-based high-resolution in-depth imaging probe capable of offering
low- and high-frequency ultrasound image pairs. We developed an attachable
dual-element EUS probe with customized low- and high-frequency ultrasound
transducers under small hardware constraints. We also designed a special geared
structure to enable the same image plane. The proposed system was evaluated
with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442
ultrasound image pairs from the tissue-mimicking phantom were acquired. We then
applied several deep learning models to obtain synthetic high-resolution
in-depth images, thus demonstrating the feasibility of our approach for
clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed
the results to find a suitable deep-learning model for our task. The obtained
results demonstrate that our proposed dual-element EUS probe with an
image-to-image translation network has the potential to provide synthetic
high-frequency ultrasound images deep inside tissues.
