2 Dimensional Ultrasound

Two-dimensional ultrasound (2D US) is a widely used imaging modality, but its 2D nature limits visualization of inherently 3D structures. Current research focuses on improving 2D US through techniques like machine learning-based 3D reconstruction from multiple 2D scans, often employing neural networks and Kalman filters to address challenges such as motion artifacts and limited data. These advancements aim to enhance diagnostic accuracy and procedural guidance, particularly in fields like obstetrics and interventional procedures, by providing more complete and reliable anatomical information. Improved image quality and efficient processing are key goals, with efforts directed towards real-time applications and accessibility in resource-limited settings.