Deep Learning for Accelerated Ultrasound Imaging

ABSTRACT In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel deep learning approach that interpolates the missing RF data by utilizing the sparsity of the RF data in the Fourier domain. Extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate without sacrificing the image quality. Index Terms-- Deep learning, ultrasound imaging, lowrank Hankel matrix 1. INTRODUCTION Due to the the excellent temporal resolution with reasonable image quality and minimal invasiveness, ultrasound imaging has been adopted as a golden-standard for many disease diagnosis in heart, liver, etc. Accordingly, there have been many research efforts to extend the US imaging to new applications such as portable imaging in emergency care, 3-D imaging, ultra-fast imaging, etc.