Hadri Hamza, Fail Abderahhim, Sadik Mohamed
1NEST Research Group, LRI Lab, ENSEM of Hassan II University of Casablanca., Casablanca, Morocco.
Med Phys. 2025 Aug;52(8):e17948. doi: 10.1002/mp.17948.
In ultrasound imaging using plane-wave (PW) techniques, image quality and contrast often suffer, especially when examining anechoic structures. Traditional beamforming methods like Delay-and-Sum or coherent PW compounding face limitations in balancing resolution and frame rate, which can result in suboptimal diagnostic accuracy.
This study aims to introduce a modular beamforming pipeline that overcomes these challenges and enhances PW image quality. By dividing the beamforming process into two modules: a multi-attention U-Net based model for capturing complex dependencies in time-delayed data and a super-resolution model for scaling up to the original B-mode image grid. We seek to improve PW image quality and modularity in the ultrasound imaging process.
We implemented a modular beamforming approach, comprising a multi-attention U-Net model and a super-resolution model. We conducted experiments using simulated, experimental, and in vivo data from the PICMUS dataset to evaluate the performance of our pipeline against conventional methods such as PW1, PW9, and U-Net. Key metrics assessed included contrast-to-noise ratio (CNR), contrast ratio (CR), generalized Contrast-to-Noise Ratio (gCNR), and resolution.
Our model demonstrated superior performance across all metrics. In simulated data, the model achieved a 0.99 improvement in CNR, a 3.5 dB increase in CR, and 18% in gCNR compared to PW1. Experimental data showed a 0.7 enhancement in CNR and a 8.6 dB improvement in CR and 24% increase in gCNR when compared to PW1. In vivo data also revealed significant improvements, with a 1.9 dB increase in CR and a 0.15 enhancement in CNR over PW1. The enhanced performance in the anechoic cyst region underscores the model's effectiveness in improving image quality.
The proposed modular beamforming approach offers significant advantages, including adaptability and improved image quality, despite the complexity of managing two models concurrently. The pipeline's flexibility in frame rate and image quality allows for customization based on specific clinical applications, making it a promising alternative to traditional methods.
在使用平面波（PW）技术的超声成像中，图像质量和对比度常常受到影响，尤其是在检查无回声结构时。像延迟求和或相干PW复合这样的传统波束形成方法在平衡分辨率和帧率方面存在局限性，这可能导致诊断准确性欠佳。
本研究旨在引入一种模块化波束形成流程，以克服这些挑战并提高PW图像质量。通过将波束形成过程分为两个模块：一个基于多注意力U-Net的模型，用于捕捉时延数据中的复杂依赖性；以及一个超分辨率模型，用于放大到原始B模式图像网格。我们力求在超声成像过程中提高PW图像质量和模块化程度。
我们实施了一种模块化波束形成方法，包括一个多注意力U-Net模型和一个超分辨率模型。我们使用来自PICMUS数据集的模拟、实验和体内数据进行实验，以评估我们的流程相对于诸如PW1、PW9和U-Net等传统方法的性能。评估的关键指标包括对比度噪声比（CNR）、对比度（CR）、广义对比度噪声比（gCNR）和分辨率。
我们的模型在所有指标上均表现出卓越性能。在模拟数据中，与PW1相比，该模型的CNR提高了0.99，CR增加了3.5 dB，gCNR提高了18%。实验数据显示，与PW1相比，CNR提高了0.7，CR提高了8.6 dB，gCNR增加了24%。体内数据也显示出显著改善，与PW1相比，CR增加了1.9 dB，CNR提高了0.15。在无回声囊肿区域的增强性能突出了该模型在改善图像质量方面的有效性。
尽管同时管理两个模型较为复杂，但所提出的模块化波束形成方法具有显著优势，包括适应性和改善的图像质量。该流程在帧率和图像质量方面的灵活性允许根据特定临床应用进行定制，使其成为传统方法的一个有前景的替代方案。
