# Deep Learning for Accelerated Ultrasound Imaging

arXiv: https://arxiv.org/abs/1710.10006 · HTML: https://mlsanity.com/paper/1710.10006/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Image translation of Ultrasound to Pseudo Anatomical Display by CycleGAN](https://mlsanity.com/paper/2202.08053/) (2022)
- [Learning-based Framework for US Signals Super-resolution](https://mlsanity.com/paper/2305.01510/) (2023)

## Abstract (arXiv)

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.