An improved ultrasound high-resolution imaging method based on spatially-variant model

Ultrasound high-resolution imaging is essential in clinic. The limited number of channels in portable ultrasound machines results in low-resolution images being acquired. Achieving high-resolution ultrasound images relies on machines with a substantial number of channels, which increases costs. Obtaining potential high-channel images from low-channel images can significantly reduce the cost of ultrasound machines. A novel physics-based deep learning method spatially-variant model (SV-Net) is proposed to deconvolve low-channel ultrasound images, yielding high-resolution outputs. SV-Net is structured with a multi-channel Wiener deconvolution layer placed before Convolutional Neural Network (CNN). The multichannel Wiener deconvolution layer consists of various differentiable Wiener deconvolutions, which leverage knowledge of spatially-variant Point Spread Functions (PSFs) in ultrasound images. These PSFs are subsequently optimized through further training to obtain high-quality ultrasound images. Experiments show the method’s efficacy in improving lateral resolution and achieving favorable performance metrics, including PSNR, SSIM, MAE, and FWHM.