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UltraGauss: Ultrafast Gaussian Reconstruction of 3D Ultrasound Volumes

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abstract

Ultrasound imaging is widely used due to its safety, affordability, and real-time capabilities, but its 2D interpretation is highly operator-dependent, leading to variability and increased cognitive demand. 2D-to-3D reconstruction mitigates these challenges by providing standardized volumetric views, yet existing methods are often computationally expensive, memory-intensive, or incompatible with ultrasound physics. We introduce UltraGauss: the first ultrasound-specific Gaussian Splatting framework, extending view synthesis techniques to ultrasound wave propagation. Unlike conventional perspective-based splatting, UltraGauss models probe-plane intersections in 3D, aligning with acoustic image formation. We derive an efficient rasterization boundary formulation for GPU parallelization and introduce a numerically stable covariance parametrization, improving computational efficiency and reconstruction accuracy. On real clinical ultrasound data, UltraGauss achieves state-of-the-art reconstructions in 5 minutes, and reaching 0.99 SSIM within 20 minutes on a single GPU. A survey of expert clinicians confirms UltraGauss' reconstructions are the most realistic among competing methods. Our CUDA implementation will be released upon publication.

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2026 1

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K-space Gaussian Representation for Parallel MRI

cs.CV · 2026-07-29 · conditional · novelty 6.0

KGR fits shared Gabor-Gaussian primitives directly in multi-coil k-space and applies low-rank projection, consistently outperforming representative parallel-MRI baselines on brain datasets.

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  • K-space Gaussian Representation for Parallel MRI cs.CV · 2026-07-29 · conditional · none · ref 26 · internal anchor

    KGR fits shared Gabor-Gaussian primitives directly in multi-coil k-space and applies low-rank projection, consistently outperforming representative parallel-MRI baselines on brain datasets.