Abstract #

Ultrasound images are corrupted by a type of signal-dependent noise, called speckle, difficult to remove or attenuate with the classical denoising methods. On the contrary, structural Magnetic Resonance Imaging (MRI) is usually a high resolution low noise image modality that involves complex and expensive equipment and long acquisition times. Herein, a deep learning-based pipeline for speckle removal in B-mode ultrasound medical images, based on cross modality transfer learning, is proposed. The architecture of the system is based on a pix2pix Generative Adversarial Network (GAN), D , able to denoise real B-mode ultrasound images by generating synthetic MRI-like versions by an image-to-image translation manner. The GAN D was trained using two classes of image pairs: i) a set consisting of authentic MRI images paired with synthetic ultrasound images generated through a dedicated ultrasound simulator based on another GAN, S , designed specifically for this purpose, and ii) a set comprising natural images paired with their corresponding noisy counterparts corrupted by Rayleigh noise. The denoising GAN proposed in this study demonstrates effective removal of speckle noise from B-mode ultrasound images. It successfully preserves the integrity of anatomical structures and avoids reconstruction artifacts, producing outputs that closely resemble typical MRI images. Comparative tests against other state-of-the-art methods reveal superior performance of the proposed denoising strategy across various reconstruction quality metrics.

PDF #

Bibtex #

@ARTICLE{10478899,
  author={Vieira, Diogo Fróis and Raposo, Afonso and Azeitona, António and Afonso, Manya V. and Pedro, Luís Mendes and Sanches, J.},
  journal={IEEE Access},
  title={Ultrasound Despeckling With GANs and Cross Modality Transfer Learning},
  year={2024},
  volume={12},
  number={},
  pages={45811-45823},
  keywords={Generative adversarial networks;Magnetic resonance imaging;Noise reduction;Speckle;Training;Ultrasonic imaging;Three-dimensional displays;Ultrasonic imaging;Deep learning;Modal analysis;Ultrasound;denoising;deep learning;GANs;modality translation},
  doi={10.1109/ACCESS.2024.3381630}
}