Open AccessUltrasound Despeckling with GANs and Cross Modality Transfer Learning
Author(s) -
Diogo Frois Vieira,
Afonso Raposo,
Antonio Azeitona,
Manya Afonso,
Luis Mendes Pedro,
J. Sanches
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3381630
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Ultrasound (US) 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 US 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 US 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 US images generated through a dedicated US 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 US 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.