Research on Ultrasound Imaging Algorithm Fused with Diffusion Model

YUAN Ye; HUANG Minshang; YANG Weifeng

doi:10.11999/JEIT251083

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YUAN Ye, HUANG Minshang, YANG Weifeng. Research on Ultrasound Imaging Algorithm Fused with Diffusion Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251083

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YUAN Ye, HUANG Minshang, YANG Weifeng. Research on Ultrasound Imaging Algorithm Fused with Diffusion Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251083

YUAN Ye, HUANG Minshang, YANG Weifeng. Research on Ultrasound Imaging Algorithm Fused with Diffusion Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251083

Citation:

YUAN Ye, HUANG Minshang, YANG Weifeng. Research on Ultrasound Imaging Algorithm Fused with Diffusion Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251083

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Research on Ultrasound Imaging Algorithm Fused with Diffusion Model

doi: 10.11999/JEIT251083 cstr: 32379.14.JEIT251083

*(Guangdong Provincial Engineering Research Center for Artificial Intelligence and Modern Ultrasonic Technology, Shantou University, Shantou 515044, China)

Received Date: 2025-10-13
Accepted Date: 2026-03-03
Rev Recd Date: 2026-03-03

Available Online: 2026-03-15

Abstract

Abstract

Objective Medical ultrasound imaging, which utilizes ultrasonic waves to probe human tissues and generates images via signal processing of the returning echoes, has become a vital clinical diagnostic tool due to its non-invasive, safe, and real-time nature. However, conventional ultrasound imaging is fundamentally limited by factors such as the finite width of ultrasonic pulses, variations in tissue acoustic impedance, and the complexity of echo signals, leading to pervasive challenges including insufficient spatial resolution, significant speckle noise, and off-axis artifacts. These limitations directly impair the detection of lesions and diagnostic accuracy. While traditional approaches focusing on hardware optimization and signal processing algorithms like adaptive beamforming have made incremental improvements, they are often constrained by physical laws, computational complexity, and reliance on manual parameter tuning. Recent deep learning-based methods, particularly those using generative adversarial networks (GANs), offer promising results but suffer from training instability and poor interpretability. The emerging diffusion model, a state-of-the-art generative paradigm, has demonstrated superior robustness and generalization in computed tomography (CT) and magnetic resonance imaging (MRI) reconstruction, yet its application in ultrasound imaging remains largely unexplored. This study aims to fill this critical gap by developing a novel diffusion model-based framework for high-quality ultrasound image formation, seeking to overcome the inherent limitations of existing methods and provide a stable, efficient, and interpretable solution for enhancing ultrasound image quality. Methods This research proposes a novel ultrasound imaging method based on a denoising diffusion probabilistic model (DDPM). The core of our approach is a multi-scale diffusion network architecture designed to progressively refine a low-quality ultrasound image (e.g., one formed by a simple Delay-and-Sum, DAS, beamformer) into a high-quality image. The process consists of a forward and a reverse process. In the forward process, Gaussian noise is gradually added to a high-quality ground-truth image over a series of timesteps. The reverse process is trained to learn the conditional denoising function. Our custom-designed denoising network takes a low-resolution DAS image as a conditional input and fuses it with the noisy image at each denoising step through residual connections and feature-wise transformations at multiple scales. This deep fusion mechanism allows the network to effectively incorporate the underlying anatomical structure from the low-quality input while iteratively removing noise and artifacts through the diffusion process. The model was trained using a dataset of paired low-quality and high-quality ultrasound images, with the high-quality images serving as the training target. The training objective was to maximize the variational lower bound on the likelihood, effectively teaching the network to reverse the noising process. The performance of the proposed method was quantitatively evaluated against traditional DAS, minimum variance (MV) beamforming, and a leading GAN-based super-resolution method using metrics including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Results and Discussions The proposed diffusion model demonstrated superior performance in enhancing ultrasound image quality. Quantitatively, our method achieved a mean PSNR of 35.2 dB and an SSIM of 0.933, representing a significant improvement of 4.5 dB in PSNR while maintaining exceptional structural fidelity compared to conventional beamforming approaches. The method also consistently outperformed adaptive minimum variance beamforming and GAN-based approaches across all evaluation metrics, including contrast-to-noise ratio. Visual assessment confirms these quantitative findings. The generated images exhibit markedly reduced speckle noise and significantly enhanced boundary clarity of anatomical structures. Critically, these improvements were achieved without introducing the blurring or artificial textures commonly observed in other deep learning-based methods. The multi-scale architecture with conditional feature injection effectively preserved structural integrity, as evidenced by the clear and continuous edges in the output.The progressive denoising nature of our approach provides inherent interpretability to the image refinement process. Unlike the opaque single-step generation of other deep learning models, our method offers transparent, step-wise enhancement from initial input to final output. Furthermore, the training process remained stable and convergent, avoiding the instability issues that frequently plague adversarial training methods. Ablation studies confirmed the critical importance of the deep fusion mechanism, while resolution analysis verified substantial improvements in both lateral and axial resolution compared to all baseline methods. Conclusions This study successfully developed and validated a novel ultrasound imaging method based on a diffusion model. The proposed framework effectively addresses key limitations in conventional and existing deep learning-based approaches. It bypasses the complex matrix computations and manual parameter tuning required by adaptive beamformers and offers a more stable training paradigm compared to GANs. The results conclusively demonstrate that the method can significantly enhance image quality by substantially improving the PSNR and maintaining excellent structural similarity, leading to images with suppressed noise, reduced artifacts, and improved resolution. The multi-scale diffusion process ensures the preservation of anatomical structures while providing a degree of interpretability to the image generation process. This work establishes diffusion models as a powerful and promising new paradigm for advanced ultrasound imaging, offering a robust and high-performance technical pathway to break through the current bottlenecks in ultrasound image quality, with potential for broad clinical impact.
- Ultrasound imaging,
- Diffusion model (DM),
- UNet,
- Difference training mechanisms

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References(26)

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