Tele-ultrasound imaging is useful in various situations. Plane wave imaging provides a method for ultrafast ultrasound with very high frame rates, which sacrifices image quality and leads to the problem of a large amount of data and low signal transmission speed in telemedicine imaging. In this paper, a novel compressive frequency-wavenumber domain beamforming method is introduced, which integrates Stolt’s f-k method and compressed sensing theory on the lateral wavenumber. The data load is reduced by the sparsity of the echo signal parallel to the transducer, which requires a smaller measurement matrix during compressed sensing to reduce memory usage and accelerate the transmission rate. The signal is compressed in the Fourier domain to obtain greater stability and better image quality after reconstruction than if it was compressed in the temporal domain. Simulated data and experimental acquisitions were used to compare compressive Fourier domain beamforming with conventional delay-and-sum (DAS) beamforming. The results showed that compressive beamforming within the wavenumber domain provides the image with higher quality from less data.

Ultrasonic B-mode imaging provides real-time and non-invasive imaging for soft tissue diagnosis in clinical use, but its limited contrast leads to the challenge of detection accuracy. Quantitative ultrasound techniques have been proposed as a promising method for soft tissue characterization and reflecting the microstructure of lesions. This study proposed a novel entropy called horizontally normalized weight-adjustable Shannon entropy (hNWASE). An adjustable weight factor was added to this entropy, so that the entropy value can be changed and the imaging performance can be adjusted to lesions according to different positions and acoustic characteristics. The relationship between the adjustable parameter n and the imaging performance was explored. Moreover, the performance of the proposed hNWASE imaging was compared with weighted Shannon entropy (WSE) imaging, and horizontally normalized Shannon entropy (hNSE) imaging by both simulations and clinical data. hNSE imaging obtained a Matthews correlation coefficient (MCC) of 0.68 ± 0.11 in the thyroid nodule diagnostic tests, which underestimated the periphery of the nodule. WSE imaging got the largest area difference of 3.70 ± 1.4 mm² between the ground truth and predicted area, which indicated that the delineation of the nodule boundary by the WSE was too large. hNWASE imaging got superior lesion area prediction with the MCC of 0.81 ± 0.06, F1 score of 0.81 ± 0.07, and generalized contrast-to-noise ratio of 0.98 ± 0.03. These findings suggested that hNWASE imaging could improve image quality and be a promising technique for tissue characterization.