# Towards CT-quality Ultrasound Imaging using Deep Learning

arXiv: https://arxiv.org/abs/1710.06304 · HTML: https://mlsanity.com/paper/1710.06304/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors](https://mlsanity.com/paper/2112.13110/) (2021)
- [Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness](https://mlsanity.com/paper/2202.01208/) (2022)
- [Ultrasound Signal Processing: From Models to Deep Learning](https://mlsanity.com/paper/2204.04466/) (2022)
- [Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation](https://mlsanity.com/paper/2202.05204/) (2022)
- [Image translation of Ultrasound to Pseudo Anatomical Display by CycleGAN](https://mlsanity.com/paper/2202.08053/) (2022)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)
- [Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging](https://mlsanity.com/paper/2410.08329/) (2024)

## Abstract (arXiv)

The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflected ultrasound radio-frequency(RF) data obtained by simulation from real CT scans of a human body. We also show that CNN is able to imitate existing computationally heavy despeckling methods, thereby saving orders of magnitude in computations and making them amenable to real-time applications.