To solve the problem of resolution and contrast in plane wave imaging (PWI), coherent plane wave compounding (CPWC) was introduced, in which scanning was performed at different angles, which can achieve the desired image quality by combining the images obtained from PWI at different angles. However, the application of this idea reduces the frame rate in proportion to the number of plane waves (PW) or angles, so that in this modality, when dealing with some applications such as shear wave imaging (SWI) and strain imaging, there is always a compromise between the frame rate and the image quality. Tensor completion (TC) is a powerful technique to recover missing information of a low-rank tensor from limited observations based on rank minimization. In this article, we present an idea based on TC to make this compromise lighter, in other words, with a smaller number of angles, we can achieve the desired quality of the output image. To evaluate the proposed idea, plane wave imaging challenge in medical ultrasound (PICMUS) datasets were used, which were recorded at 75 different angles. The results of the resolution evaluation showed that using 20% of the coherent plane waves and reconstructing other 80% by TC, compared to the situation of using only 20% of the coherent plane waves provided a resolution improvement of 14.97% and 17.4% in simulated and experimental point targets, respectively. Also, the results of the contrast investigation showed that the contrast ratio (CR) improved by 72.6%, 62.9%, and 111.4% in the simulated cyst target data, experimental cyst targets, and in vivo carotid cross-section, respectively. The results confirmed that by using 20% of the coherent plane waves and reconstructing other 80% by TC, image quality is very close to that of obtained by considering all 75 angles, so that the difference in resolution is less than 2% and the difference in contrast to noise ratio (CNR) is less than 5dB. Therefore, with this idea, it can be said that less compromise is needed, in other words, despite having a higher frame rate, an acceptable quality can be achieved.

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET’s capability for precise segmentation and PatchGAN’s capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.