# CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging

arXiv: https://arxiv.org/abs/2008.12750 · HTML: https://mlsanity.com/paper/2008.12750/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2307.15990/) (2023)
- [Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors](https://mlsanity.com/paper/2203.10078/) (2022)
- [Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2408.10987/) (2024)
- [Fast Sampling generative model for Ultrasound image reconstruction](https://mlsanity.com/paper/2312.09510/) (2023)
- [Ultrasound Imaging based on the Variance of a Diffusion Restoration Model](https://mlsanity.com/paper/2403.15316/) (2024)
- [Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty](https://mlsanity.com/paper/2310.20618/) (2023)
- [Single PW takes a shortcut to compound PW in US imaging](https://mlsanity.com/paper/2312.09514/) (2023)
- [Ultrasound Image Enhancement with the Variance of Diffusion Models](https://mlsanity.com/paper/2409.11380/) (2024)
- [Lightweight Physics-Aware Zero-Shot Ultrasound Plane-Wave Denoising](https://mlsanity.com/paper/2506.21499/) (2025)

## Abstract (arXiv)

Ultrafast ultrasound (US) revolutionized biomedical imaging with its capability of acquiring full-view frames at over 1 kHz, unlocking breakthrough modalities such as shear-wave elastography and functional US neuroimaging. Yet, it suffers from strong diffraction artifacts, mainly caused by grating lobes, side lobes, or edge waves. Multiple acquisitions are typically required to obtain a sufficient image quality, at the cost of a reduced frame rate. To answer the increasing demand for high-quality imaging from single unfocused acquisitions, we propose a two-step convolutional neural network (CNN)-based image reconstruction method, compatible with real-time imaging. A low-quality estimate is obtained by means of a backprojection-based operation, akin to conventional delay-and-sum beamforming, from which a high-quality image is restored using a residual CNN with multiscale and multichannel filtering properties, trained specifically to remove the diffraction artifacts inherent to ultrafast US imaging. To account for both the high dynamic range and the oscillating properties of radio frequency US images, we introduce the mean signed logarithmic absolute error (MSLAE) as a training loss function. Experiments were conducted with a linear transducer array, in single plane-wave (PW) imaging. Trainings were performed on a simulated dataset, crafted to contain a wide diversity of structures and echogenicities. Extensive numerical evaluations demonstrate that the proposed approach can reconstruct images from single PWs with a quality similar to that of gold-standard synthetic aperture imaging, on a dynamic range in excess of 60 dB. In vitro and in vivo experiments show that trainings carried out on simulated data perform well in experimental settings.