超声计算机断层扫描（UCT）因其在早期乳腺癌诊断和筛查方面的潜力而受到越来越多的关注。合成孔径成像是一种广泛使用的反射 UCT 图像重建手段，因为它能够产生各向同性和高分辨率的解剖图像。然而，通过多次传输从各个方向获取完全采样的 UCT 数据是一个耗时的扫描过程。尽管稀疏传输策略可以减轻数据采集的复杂性，但通过传统的延迟求和（DAS）方法重建的图像质量可能会大幅下降。本研究提出了一种基于条件生成对抗网络 UCT-GAN 的深度学习框架，可从稀疏传输数据中有效地重建反射 UCT 图像。使用体内乳腺成像数据的评估实验表明，所提出的 UCT-GAN 仅使用 8 次传输就能够生成高质量的反射 UCT 图像，这与根据 512 次传输获取的数据重建的图像相当。峰值信噪比（PSNR）、归一化均方误差（NMSE）和结构相似性指数测量（SSIM）方面的定量评估表明，所提出的 UCT-GAN 能够有效地重建高质量的反射 UCT 图像来自稀疏的可用传输数据，优于其他几种方法，例如 RED-GAN、DnCNN-GAN、BM3D。在8次传输稀疏数据的实验中，PSNR为29.52dB，SSIM为0.7619。所提出的方法有可能集成到 UCT 成像系统中以供临床使用。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Ultrasound computed tomography (UCT) has attracted increasing attention due to its potential for early breast cancer diagnosis and screening. Synthetic aperture imaging is a widely used means for reflection UCT image reconstruction, due to its ability to produce isotropic and high-resolution anatomical images. However, obtaining fully sampled UCT data from all directions over multiple transmissions is a time-consuming scanning process. Even though sparse transmission strategy could mitigate the data acquisition complication, image quality reconstructed by traditional Delay and Sum (DAS) methods may degrade substantially. This study presents a deep learning framework based on a conditional generative adversarial network, UCT-GAN, to efficiently reconstruct reflection UCT image from sparse transmission data. The evaluation experiments using breast imaging data in vivo show that the proposed UCT-GAN is able to generate high-quality reflection UCT images when using 8 transmissions only, which are comparable to that reconstructed from the data acquired by 512 transmissions. Quantitative assessment in terms of peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and structural similarity index measurement (SSIM) show that the proposed UCT-GAN is able to efficiently reconstruct high-quality reflection UCT images from sparsely available transmission data, outperforming several other methods, such as RED-GAN, DnCNN-GAN, BM3D. In the experiment of 8-transmission sparse data, the PSNR is 29.52 dB, and the SSIM is 0.7619. The proposed method has the potential of being integrated into the UCT imaging system for clinical usage.Copyright © 2024 Elsevier B.V. All rights reserved.