NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild
This addresses the challenge of producing reliable 3D reconstructions from casually captured ultrasound data in clinical settings, which is an incremental improvement over existing methods.
The paper tackled the problem of severe artifacts in 3D reconstruction and novel view synthesis for ultrasound imaging data using NeRF-based methods, resulting in accurate, clinically plausible, and artifact-free reconstructions as demonstrated on a new dataset.
Current methods for performing 3D reconstruction and novel view synthesis (NVS) in ultrasound imaging data often face severe artifacts when training NeRF-based approaches. The artifacts produced by current approaches differ from NeRF floaters in general scenes because of the unique nature of ultrasound capture. Furthermore, existing models fail to produce reasonable 3D reconstructions when ultrasound data is captured or obtained casually in uncontrolled environments, which is common in clinical settings. Consequently, existing reconstruction and NVS methods struggle to handle ultrasound motion, fail to capture intricate details, and cannot model transparent and reflective surfaces. In this work, we introduced NeRF-US, which incorporates 3D-geometry guidance for border probability and scattering density into NeRF training, while also utilizing ultrasound-specific rendering over traditional volume rendering. These 3D priors are learned through a diffusion model. Through experiments conducted on our new "Ultrasound in the Wild" dataset, we observed accurate, clinically plausible, artifact-free reconstructions.