UltraG-Ray: Physics-Based Gaussian Ray Casting for Novel Ultrasound View Synthesis
This work addresses the challenge of generating anatomically plausible ultrasound views for training clinicians or data augmentation, representing an incremental improvement over existing methods.
The authors tackled the problem of novel view synthesis in ultrasound imaging by introducing UltraG-Ray, a method that uses a learnable 3D Gaussian field and physics-based ray casting to generate realistic B-mode images, achieving up to a 15% increase in MS-SSIM metrics.
Novel view synthesis (NVS) in ultrasound has gained attention as a technique for generating anatomically plausible views beyond the acquired frames, offering new capabilities for training clinicians or data augmentation. However, current methods struggle with complex tissue and view-dependent acoustic effects. Physics-based NVS aims to address these limitations by including the ultrasound image formation process into the simulation. Recent approaches combine a learnable implicit scene representation with an ultrasound-specific rendering module, yet a substantial gap between simulation and reality remains. In this work, we introduce UltraG-Ray, a novel ultrasound scene representation based on a learnable 3D Gaussian field, coupled to an efficient physics-based module for B-mode synthesis. We explicitly encode ultrasound-specific parameters, such as attenuation and reflection, into a Gaussian-based spatial representation and realize image synthesis within a novel ray casting scheme. In contrast to previous methods, this approach naturally captures view-dependent attenuation effects, thereby enabling the generation of physically informed B-mode images with increased realism. We compare our method to state-of-the-art and observe consistent gains in image quality metrics (up to 15% increase on MS-SSIM), demonstrating clear improvement in terms of realism of the synthesized ultrasound images.