Deep Coherence Learning: An Unsupervised Deep Beamformer for High
  Quality Single Plane Wave Imaging in Medical Ultrasound
Hyunwoo Cho Seongjun Park Jinbum Kang Yangmo Yoo
Plane wave imaging (PWI) in medical ultrasound is becoming an important
reconstruction method with high frame rates and new clinical applications.
Recently, single PWI based on deep learning (DL) has been studied to overcome
lowered frame rates of traditional PWI with multiple PW transmissions. However,
due to the lack of appropriate ground truth images, DL-based PWI still remains
challenging for performance improvements. To address this issue, in this paper,
we propose a new unsupervised learning approach, i.e., deep coherence learning
(DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the
DL network is trained to predict highly correlated signals with a unique loss
function from a set of PW data, and the trained DL model encourages
high-quality PWI from low-quality single PW data. In addition, the DL-DCL
framework based on complex baseband signals enables a universal beamformer. To
assess the performance of DL-DCL, simulation, phantom and in vivo studies were
conducted with public datasets, and it was compared with traditional
beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based
methods (i.e., supervised learning approach with 1-PW and generative
adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL
showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial
resolution, and it outperformed all comparison methods in contrast resolution.
These results demonstrated that the proposed unsupervised learning approach can
address the inherent limitations of traditional PWIs based on DL, and it also
showed great potential in clinical settings with minimal artifacts.