Image quality improvement in single plane-wave imaging using deep learning.

In ultrasound image diagnosis, single plane-wave imaging (SPWI), which can acquire ultrasound images at more than 1000 fps, has been used to observe detailed tissue and evaluate blood flow. SPWI achieves high temporal resolution by sacrificing the spatial resolution and contrast of ultrasound images. To improve spatial resolution and contrast in SPWI, coherent plane-wave compounding (CPWC) is used to obtain high-quality ultrasound images, i.e., compound images, by coherent addition of radio frequency (RF) signals acquired by transmitting plane waves in different directions. Although CPWC produces high-quality ultrasound images, their temporal resolution is lower than that of SPWI. To address this problem, some methods have been proposed to reconstruct a ultrasound image comparable to a compound image from RF signals obtained by transmitting a small number of plane waves in different directions. These methods do not fully consider the properties of RF signals, resulting in lower image quality compared to a compound image. In this paper, we propose methods to reconstruct high-quality ultrasound images in SPWI by considering the characteristics of RF signal of a single plane wave to obtain ultrasound images with image quality comparable to CPWC. The proposed methods employ encoder-decoder models of 1D U-Net, 2D U-Net, and their combination to generate the high-quality ultrasound images by minimizing the loss that considers the point spread effect of plane waves and frequency spectrum of RF signals in training. We also create a public large-scale SPWI/CPWC dataset for developing and evaluating deep-learning methods. Through a set of experiments using the public dataset and our dataset, we demonstrate that the proposed methods can reconstruct higher-quality ultrasound images from RF signals in SPWI than conventional method.