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

Funds: The National Natural Science of China (82071992)

Received Date: 2025-10-13
Accepted Date: 2026-03-03
Rev Recd Date: 2026-03-01

Available Online: 2026-03-15

Abstract

Abstract

Objective Medical ultrasound imaging uses ultrasonic waves to probe human tissues and forms images by processing returning echoes. It has become an essential clinical diagnostic tool because it is noninvasive, safe, and capable of real-time imaging. However, conventional ultrasound imaging remains fundamentally limited by factors such as the finite width of ultrasonic pulses, variations in tissue acoustic impedance, and the complexity of echo signals. These factors lead to persistent challenges, including limited spatial resolution, severe speckle noise, and off-axis artifacts. These limitations directly reduce lesion detectability and diagnostic accuracy. Traditional approaches based on hardware optimization and signal processing algorithms, such as adaptive beamforming, have provided only incremental improvement. Their performance is often constrained by physical laws, computational complexity, and dependence on manual parameter tuning. Recent deep learning methods, particularly those based on Generative Adversarial Networks (GANs), have shown promising performance, but they suffer from training instability and limited interpretability. The diffusion model, an emerging state-of-the-art generative framework, has shown strong robustness and generalization in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) reconstruction. However, its application in ultrasound imaging remains largely unexplored. This study aims to address this gap by developing a novel diffusion model-based framework for high-quality ultrasound image formation and to provide a stable, efficient, and interpretable solution for improving ultrasound image quality. Methods A novel ultrasound imaging method based on a Denoising Diffusion Probabilistic Model (DDPM) is proposed. The core of the method is a multi-scale diffusion network architecture designed to progressively refine a low-quality ultrasound image, such as one generated by a simple Delay-And-Sum (DAS) beamformer, into a high-quality image. The process includes forward and reverse stages. In the forward stage, Gaussian noise is gradually added to a high-quality ground-truth image over a series of time steps. In the reverse stage, the model is trained to learn the conditional denoising function. A custom denoising network takes a low-resolution DAS image as 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 enables the network to incorporate the underlying anatomical structure from the low-quality input while iteratively removing noise and artifacts through the diffusion process. The model is trained on a dataset of paired low-quality and high-quality ultrasound images, in which the high-quality images serve as the training target. The training objective is to maximize the variational lower bound of the likelihood, thereby enabling the network to reverse the noising process. The proposed method is quantitatively compared with traditional DAS, Minimum Variance (MV) beamforming, and a representative GAN-based super-resolution method using Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity Index (SSIM). Results and Discussions The proposed diffusion model demonstrates superior performance in improving ultrasound image quality. Quantitatively, the method achieves a mean PSNR of 35.2 dB and an SSIM of 0.933, with a PSNR improvement of 4.5 dB over conventional beamforming methods, while maintaining excellent structural fidelity. The method also consistently outperforms adaptive MV beamforming and GAN-based methods across all evaluation metrics, including contrast-to-noise ratio. Visual assessment supports these quantitative results. The generated images show markedly reduced speckle noise and substantially improved boundary definition of anatomical structures. Notably, these improvements are achieved without the blurring or artificial textures commonly observed in other deep learning-based methods. The multi-scale architecture with conditional feature injection effectively preserves structural integrity, as shown by the clear and continuous edges in the output images. The progressive denoising nature of the method also provides inherent interpretability for the image refinement process. Unlike the opaque single-step generation used in many other deep learning models, this method provides a transparent, stepwise enhancement pathway from the initial input to the final output. In addition, the training process remains stable and convergent, avoiding the instability that frequently affects adversarial training methods. Ablation experiments confirm the critical role of the deep fusion mechanism, and resolution analysis verifies substantial improvement in both lateral and axial resolution compared with all baseline methods. Conclusions This study develops and validates a novel ultrasound imaging method based on a diffusion model. The proposed framework effectively addresses key limitations of conventional methods and existing deep learning-based approaches. It avoids the complex matrix computations and manual parameter tuning required by adaptive beamformers and provides a more stable training framework than GAN-based methods. The results show that the method can substantially improve image quality by increasing PSNR and maintaining excellent structural similarity, thereby producing images with suppressed noise, reduced artifacts, and improved resolution. The multi-scale diffusion process preserves anatomical structures and provides a degree of interpretability for the image generation process. This work establishes diffusion models as a promising new framework for advanced ultrasound imaging and provides a robust, high-performance technical route for overcoming current bottlenecks in ultrasound image quality, with broad potential clinical value.
- Ultrasound imaging,
- Diffusion Model (DM),
- UNet,
- Difference training mechanism

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References(26)

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