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

TL;DR

The proposed model reconstructs speckle-suppressed images while preserving anatomically meaningful edges and contrast, and quantifies prediction uncertainty via cross-model variance and shows that higher uncertainty correlates with higher reconstruction error, providing a practical indicator of difficult or failure-prone regions.

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.

Figures (10)

• Figure 1: Overview of our Image Restoration SDE (IRSDE) ultrasound despeckling pipeline. The forward image degradation SDE progressively perturbs a HQ image $x(0)$ into a terminal state $x(T)$, which is approximately a noisy observation of the low-quality image. The reverse image restoration SDE then evolves $x(T)$ back towards $x(0)$ by simulating the corresponding reverse-time dynamics, where the drift includes the score term $\nabla_x \log p_t(x)$, which is parameterized via a conditional time dependent U-Net noise predictor $\hat{\epsilon}_\phi(x_t,\mu,t)$.
• Figure 1: Qualitative comparison on ultrasound data simluated from full-view MRI 2D slices. Columns show the low-quality (LQ) speckled input, high-quality (HQ) reference patch, and outputs from baseline methods and IRSDE Despeckle. Due to space limitations, we show selected learning-based baselines. The examples illustrate tradeoffs between speckle suppression and structural preservation.
• Figure 2: Overview of our data generation scheme. a) MRI slices were segmented and augmented to maximize anatomically informative content. b) The processed 2D grayscale MRI samples were simulated into B-mode US images using the MUST toolbox. The pipeline output paired high quality and low quality images for training.
• Figure 2: Qualitative comparison on ultrasound data simulated from ROI patches of MRI 2D slices. Columns show the low-quality (LQ) speckled input, high-quality (HQ) reference patch, and outputs from baseline methods and IRSDE Despeckle. Due to space limitations, we show selected learning-based baselines. The examples illustrate tradeoffs between speckle suppression and structural preservation.
• Figure 3: Visual comparison of despeckling on a simulated ultrasound image from the held-out test dataset from UMD. Columns show the paired LQ--HQ images, our IRSDE despeckling result, and representative comparisons (DnCNN, OBNLM, MSRResNet, Speckle2Self, and ultrasound denoising GAN). The red boxes in the top row mark an example region of interest, and the corresponding zoomed-in croped images are shown in the bottom row. Our method suppresses speckle while preserving edges and contrast in the highlighted region, whereas several comparison SOTA methods either leave residual speckle or oversmooth fine structures and attenuate local contrast.