High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models

• URL: http://arxiv.org/abs/2505.22090v1
• Date: Wed, 28 May 2025 08:14:12 GMT
• Title: High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models
• Authors: Tristan S. W. Stevens, Oisín Nolan, Oudom Somphone, Jean-Luc Robert, Ruud J. G. van Sloun,
• Abstract summary: Three-dimensional ultrasound enables real-time volumetric visualization of anatomical structures.<n> achieving both high volume rates and high image quality remains a significant challenge.<n>This paper introduces a novel approach to 3D ultrasound reconstruction by employing diffusion models (DMs) to achieve increased spatial and temporal resolution.
• Score: 14.007660129016852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Three-dimensional ultrasound enables real-time volumetric visualization of anatomical structures. Unlike traditional 2D ultrasound, 3D imaging reduces the reliance on precise probe orientation, potentially making ultrasound more accessible to clinicians with varying levels of experience and improving automated measurements and post-exam analysis. However, achieving both high volume rates and high image quality remains a significant challenge. While 3D diverging waves can provide high volume rates, they suffer from limited tissue harmonic generation and increased multipath effects, which degrade image quality. One compromise is to retain the focusing in elevation while leveraging unfocused diverging waves in the lateral direction to reduce the number of transmissions per elevation plane. Reaching the volume rates achieved by full 3D diverging waves, however, requires dramatically undersampling the number of elevation planes. Subsequently, to render the full volume, simple interpolation techniques are applied. This paper introduces a novel approach to 3D ultrasound reconstruction from a reduced set of elevation planes by employing diffusion models (DMs) to achieve increased spatial and temporal resolution. We compare both traditional and supervised deep learning-based interpolation methods on a 3D cardiac ultrasound dataset. Our results show that DM-based reconstruction consistently outperforms the baselines in image quality and downstream task performance. Additionally, we accelerate inference by leveraging the temporal consistency inherent to ultrasound sequences. Finally, we explore the robustness of the proposed method by exploiting the probabilistic nature of diffusion posterior sampling to quantify reconstruction uncertainty and demonstrate improved recall on out-of-distribution data with synthetic anomalies under strong subsampling.

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