IRSDE-Despeckle: A Physics-Grounded Diffusion Model for Generalizable Ultrasound Despeckling

• URL: http://arxiv.org/abs/2602.22717v1
• Date: Thu, 26 Feb 2026 07:42:25 GMT
• Title: IRSDE-Despeckle: A Physics-Grounded Diffusion Model for Generalizable Ultrasound Despeckling
• Authors: Shuoqi Chen, Yujia Wu, Geoffrey P. Luke,
• Abstract summary: We present a diffusion-based ultrasound despeckling method built on the Image Restoration Differential Equations framework.<n>To enable supervised training, we curate large paired datasets by simulating ultrasound images from speckle-free magnetic resonance images.<n>The proposed model reconstructs speckle-suppressed images anatomically meaningful edges and contrast.
• Score: 0.2580765958706854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultrasound imaging is widely used for real-time, noninvasive diagnosis, but speckle and related artifacts reduce image quality and can hinder interpretation. We present a diffusion-based ultrasound despeckling method built on the Image Restoration Stochastic Differential Equations framework. To enable supervised training, we curate large paired datasets by simulating ultrasound images from speckle-free magnetic resonance images using the Matlab UltraSound Toolbox. The proposed model reconstructs speckle-suppressed images while preserving anatomically meaningful edges and contrast. On a held-out simulated test set, our approach consistently outperforms classical filters and recent learning-based despeckling baselines. We quantify prediction uncertainty via cross-model variance and show that higher uncertainty correlates with higher reconstruction error, providing a practical indicator of difficult or failure-prone regions. Finally, we evaluate sensitivity to simulation probe settings and observe domain shift, motivating diversified training and adaptation for robust clinical deployment.

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