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How could ultrasounds be improved? 


Best insight from top research papers

Ultrasound imaging can be improved in several ways. One approach is to use ultrasound localization microscopy (ULM) with cumulant analysis, which improves imaging performance by eliminating axial oscillations and achieving higher spatial resolution . Another method is to use Convolutional Neural Networks (CNN) to enhance image quality in handheld ultrasound devices, resulting in high-quality reconstruction images with fine details and structure . Portable ultrasound devices can also benefit from a generative adversarial network (GAN) model that maps low-quality ultrasound images to high-quality ones, reconstructing tissue structure, details, and speckle for improved diagnostic reliability . Additionally, a method based on CEEMDAN can be used to denoise ultrasound images, improving sharpness and contrast . Finally, an ultrasound image's quality can be improved by analyzing and amending strong sampled signal regions using a method that partitions edges and determines strong and weak signal triangle regions .

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The paper proposes a two-stage generative adversarial network (GAN) model to improve the image quality of portable ultrasound devices.
The paper proposes a new method based on CEEMDAN to denoise ultrasound images, resulting in improved image quality in terms of SSIM and PSNR.
The provided paper describes a method for improving the quality of an ultrasound image by analyzing and processing the ultrasound sampled signals.
The paper suggests using Convolutional Neural Networks (CNN) to improve the image quality of handheld ultrasound devices, resulting in high-quality reconstruction images with fine details, structure, and speckle.
The paper proposes a method for improving ultrasound imaging performance by using complex cumulant analysis, which eliminates axial oscillations and achieves higher spatial resolution and faster data acquisition speed.

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