EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise

This is the official repository for the following paper (ICCV 2025 Workshop CVAMD):

EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
https://arxiv.org/abs/2505.00043

Abstract: Echocardiography (echo), or cardiac ultrasound, is the most widely used imaging modality for cardiac form and function due to its relatively low cost, rapid acquisition time, and non-invasive nature. However, ultrasound acquisitions are often limited by artifacts and noise that hinder diagnostic interpretation in clinical settings. Existing methodologies for denoising echos consist solely of traditional filtering-based algorithms or deep learning methods developed on radio-frequency (RF) signals which prevents clinical applicability and scalability. To address these limitations, we introduce the first deep generative model capable of simulating ultrasound noise developed on B-mode data. Using this generative model, we develop a synthetic dataset of paired clean and noisy echo images to train a downstream model for real-world image denoising and demonstrate state-of-the-art performance in both internal and external experiments. In both held-out test sets, our method results in echo images with higher gCNR in comparison to noisy image counterparts and images derived from a comparable method which is consistent with provided visual comparisons. Our experiments showcase the potential of our method for future clinical use to improve the quality of echo acquisitions.

Noise Simulation

Image Denoising

Physician-Labeled Noisy	Denoised

Inference

1. Download model weights from release.

2. For denoising, run the following command:

python3 denoise.py --unet [...] --input [...] --output [...]

• unet: path to pretrained weights for U-Net (.pt)
• input: path to folder containing noisy A4C echo images (.png)
• output: path to folder that will store denoised A4C echo images (.png)

3. For noise simulation, run the following command:

python3 simulate_noise.py --gan [...] --encoder [...] --input [...] --output [...] --global [...] --center_field [...] --near_field [...]

• gan: path to pretrained weights for StyleGAN (.pkl)
• encoder: path to pretrained weights for Encoder4Editing (.pt)
• input: path to file containing a clean A4C echo image (.png)
• output: path to folder that will store noise simulation results (.png)
• global: integer that controls the extent of global noise
• center_field: integer that controls the extent of center-field noise
• near_field: integer that controls the extent near-field noise

Acknowledgements

This repository builds upon StyleGAN2 and e4e.