Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning
This addresses the need for efficient image reconstruction in ultrasound systems with limited measurements, though it appears incremental as it builds on deep learning for a known bottleneck.
The paper tackles the problem of reconstructing high-quality B-mode ultrasound images from sub-sampled RF data, which causes blurry images with artifacts in portable and fast imaging systems. The proposed deep learning method reduces the data rate without sacrificing image quality, as confirmed by experimental results.
In portable, three dimensional, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe artifacts from RF sub-sampling, the standard beamformer often produces blurry images with less contrast, which are unsuitable for diagnostic purposes. Existing compressed sensing approaches often require either hardware changes or computationally expensive algorithms, but their quality improvements are limited. To address this problem, here we propose a novel deep learning approach that directly interpolates the missing RF data by utilizing redundancy in the Rx-Xmit plane. Our extensive experimental results using sub-sampled RF data from a multi-line acquisition B-mode system confirm that the proposed method can effectively reduce the data rate without sacrificing image quality.