Neural Radiance Fields For Ultrasound Imaging with Image-to-Image Refinements

Novel view synthesis in ultrasound imaging enables clinicians to visualize additional perspectives from existing scans, enhancing the interpretation of complex anatomical features without the need for further imaging. Traditional methods, however, often lead to compromised image quality, manifesting as blurring and loss of detail. In this study, we introduce an advanced framework that integrates neural radiance fields (NeRF) with generative adversarial networks (GANs) to produce ultrasound images with enhanced detail and improved tissue clarity. This framework combines the physics-informed neural representations of NeRF for initial image generation with a GAN-driven refinement process to improve image quality. Our comprehensive qualitative and quantitative evaluations demonstrate that our method achieves superior performance compared to existing techniques.