  Three-dimensional ultrasound imaging is a critical technology widely used in
medical diagnostics. However, traditional 3D ultrasound imaging methods have
limitations such as fixed resolution, low storage efficiency, and insufficient
contextual connectivity, leading to poor performance in handling complex
artifacts and reflection characteristics. Recently, techniques based on NeRF
(Neural Radiance Fields) have made significant progress in view synthesis and
3D reconstruction, but there remains a research gap in high-quality ultrasound
imaging. To address these issues, we propose a new model, UlRe-NeRF, which
combines implicit neural networks and explicit ultrasound volume rendering into
an ultrasound neural rendering architecture. This model incorporates reflection
direction parameterization and harmonic encoding, using a directional MLP
module to generate view-dependent high-frequency reflection intensity
estimates, and a spatial MLP module to produce the medium's physical property
parameters. These parameters are used in the volume rendering process to
accurately reproduce the propagation and reflection behavior of ultrasound
waves in the medium. Experimental results demonstrate that the UlRe-NeRF model
significantly enhances the realism and accuracy of high-fidelity ultrasound
image reconstruction, especially in handling complex medium structures.
