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Plane wave imaging (PWI) in medical ultrasound is becoming an importantreconstruction method with high frame rates and new clinical applications.Recently, single PWI based on deep learning (DL) has been studied to overcomelowered frame rates of traditional PWI with multiple PW transmissions. However,due to the lack of appropriate ground truth images, DL-based PWI still remainschallenging 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, theDL network is trained to predict highly correlated signals with a unique lossfunction from a set of PW data, and the trained DL model encourageshigh-quality PWI from low-quality single PW data. In addition, the DL-DCLframework based on complex baseband signals enables a universal beamformer. Toassess the performance of DL-DCL, simulation, phantom and in vivo studies wereconducted with public datasets, and it was compared with traditionalbeamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-basedmethods (i.e., supervised learning approach with 1-PW and generativeadversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCLshowed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatialresolution, and it outperformed all comparison methods in contrast resolution.These results demonstrated that the proposed unsupervised learning approach canaddress the inherent limitations of traditional PWIs based on DL, and it alsoshowed great potential in clinical settings with minimal artifacts.

Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound