Ultrasound Volume

Research on ultrasound volume focuses on efficiently and accurately reconstructing three-dimensional (3D) ultrasound images from various input sources, including 2D scans and existing 3D volumes. Current efforts leverage deep learning models, such as neural radiance fields (NeRFs) and variations of U-Net and residual networks, to improve reconstruction speed, accuracy, and handling of complex artifacts. These advancements are significantly impacting medical imaging, enabling more precise diagnoses and improved guidance for minimally invasive procedures like liver surgery and fetal brain monitoring. The development of robust and efficient 3D ultrasound reconstruction methods holds considerable promise for advancing medical care.