Khalifa Mostafa, Hamza Hanaa M, Hosny Khalid M
Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt.
Sci Rep. 2025 Jul 3;15(1):23703. doi: 10.1038/s41598-025-07115-1.
Ultrasound imaging provides real-time views of internal organs, which are essential for accurate diagnosis and treatment. However, speckle noise, caused by wave interactions with tissues, creates a grainy texture that hides crucial details. This noise varies with image intensity, which limits the effectiveness of traditional denoising methods. We introduce the Metric-Optimized Knowledge Distillation (MK) model, a deep-learning approach that utilizes Knowledge Distillation (KD) for denoising ultrasound images. Our method transfers knowledge from a high-performing teacher network to a smaller student network designed for this task. By leveraging KD, the model removes speckle noise while preserving key anatomical details needed for accurate diagnosis. A key innovation of our paper is the metric-guided training strategy. We achieve this by repeatedly computing evaluation metrics used to assess our model. Incorporating them into the loss function enables the model to reduce noise and enhance image quality optimally. We evaluate our proposed method against state-of-the-art despeckling techniques, including DNCNN and other recent models. The results demonstrate that our approach performs superior noise reduction and image quality preservation, making it a valuable tool for enhancing the diagnostic utility of ultrasound images.
超声成像可提供内部器官的实时视图，这对于准确诊断和治疗至关重要。然而，由波与组织相互作用引起的斑点噪声会产生一种颗粒状纹理，掩盖了关键细节。这种噪声随图像强度而变化，这限制了传统去噪方法的有效性。我们引入了度量优化知识蒸馏（MK）模型，这是一种利用知识蒸馏（KD）对超声图像进行去噪的深度学习方法。我们的方法将知识从高性能的教师网络转移到为该任务设计的较小的学生网络。通过利用KD，该模型在去除斑点噪声的同时保留了准确诊断所需的关键解剖细节。我们论文的一个关键创新是度量引导训练策略。我们通过反复计算用于评估我们模型的评估指标来实现这一点。将它们纳入损失函数使模型能够最佳地降低噪声并提高图像质量。我们将我们提出的方法与包括DNCNN和其他近期模型在内的最先进的去噪技术进行了评估。结果表明，我们的方法在降噪和图像质量保留方面表现出色，使其成为提高超声图像诊断效用的有价值工具。
