IEEE Trans Ultrason Ferroelectr Freq Control. 2023 Sep;70(9):1084-1100. doi: 10.1109/TUFFC.2023.3299954. Epub 2023 Aug 29.
Ultrasound computed tomography (USCT) is a fast-emerging imaging modality that is expected to help detect and characterize breast tumors by quantifying the distribution of the speed of sound (SOS) and acoustic attenuation in breast tissue. High-quality quantitative SOS reconstruction in USCT requires a large number of transducers, which incurs high system costs and slow computation. In contrast, sparsely distributed arrays are low-cost and fast but significantly degrade image quality. Thus, we propose a framework to achieve high-quality SOS reconstruction under sparse sampling based on a convolutional neural network (SRSS-Net) with faster computation. We first apply the bent-ray algorithm to sparsely sampled data and then apply the SRSS-Net to efficiently improve the image quality. Experimental results on synthetic and real datasets demonstrate that the proposed SRSS-Net provides reconstructions that are superior to those of state-of-the-art methods in terms of artifact suppression, structural preservation, quantitative restoration, and computational speed. As demonstrated in our experiments, the fine-tuning training strategy is suggested when applying SRSS-Net to real-world circumstances. The imaging and computational performance of SRSS-Net on the inhomogeneous breast phantom further demonstrates that SRSS-Net has great potential in real-time breast cancer detection.
超声计算机断层成像（USCT）是一种快速发展的成像方式，有望通过量化乳腺组织中声速（SOS）和声波衰减的分布来帮助检测和表征乳腺肿瘤。USCT 中高质量的定量 SOS 重建需要大量的换能器，这会导致系统成本高和计算速度慢。相比之下，稀疏分布的阵列成本低、速度快，但会显著降低图像质量。因此，我们提出了一种基于卷积神经网络（SRSS-Net）的框架，在稀疏采样的基础上实现高质量的 SOS 重建，具有更快的计算速度。我们首先将弯曲射线算法应用于稀疏采样数据，然后应用 SRSS-Net 来有效地提高图像质量。在合成和真实数据集上的实验结果表明，与最先进的方法相比，所提出的 SRSS-Net 在抑制伪影、保持结构、定量恢复和计算速度方面提供了更好的重建效果。如我们的实验所示，当将 SRSS-Net 应用于实际情况时，建议采用微调训练策略。SRSS-Net 在非均匀乳腺体模上的成像和计算性能进一步表明，SRSS-Net 在实时乳腺癌检测方面具有很大的潜力。
