# Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning

arXiv: https://arxiv.org/abs/1712.06096 · HTML: https://mlsanity.com/paper/1712.06096/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2017.

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness](https://mlsanity.com/paper/2202.01208/) (2022)
- [Deep Learning for Ultrasound Beamforming](https://mlsanity.com/paper/2109.11431/) (2021)
- [USEFUSE: Uniform Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks](https://mlsanity.com/paper/2412.13724/) (2024)
- [Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in Ultrasound Imaging](https://mlsanity.com/paper/2501.03825/) (2025)
- [Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline](https://mlsanity.com/paper/2110.10220/) (2021)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)
- [Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses](https://mlsanity.com/paper/2211.01131/) (2022)
- [Physics-Guided Multi-Objective Deep Learning for Ultrasound RF Data Interpolation in Resource-Constrained Imaging](https://mlsanity.com/paper/2609.28775/) (2026)

## Abstract (arXiv)

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.