With balanced spatial resolution, penetration depth, and imaging speed, photoacoustic computed tomography (PACT) is promising for clinical translation such as in breast cancer screening, functional brain imaging, and surgical guidance. Typically using a linear ultrasound (US) transducer array, PACT has great flexibility for hand-held applications. However, the linear US transducer array has a limited detection angle range and frequency bandwidth, resulting in limited-view and limited-bandwidth artifacts in the reconstructed PACT images. These artifacts significantly reduce the imaging quality. To address these issues, existing solutions often have to pay the price of system complexity, cost, and/or imaging speed. Here, we propose a deep-learning-based method that explores the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to reduce the limited-view and limited-bandwidth artifacts in PACT. Compared with existing reconstruction and convolutional neural network approach, our model has shown improvement in imaging quality and resolution. Our results on simulation, phantom, and in vivo data have collectively demonstrated the feasibility of applying WGAN-GP to improve PACT’s image quality without any modification to the current imaging set-up.

Impact statement

This study has the following main impacts. It offers a promising solution for removing limited-view and limited-bandwidth artifact in PACT using a linear-array transducer and conventional image reconstruction, which have long hindered its clinical translation. Our solution shows unprecedented artifact removal ability for in vivo image, which may enable important applications such as imaging tumor angiogenesis and hypoxia. The study reports, for the first time, the use of an advanced deep-learning model based on stabilized generative adversarial network. Our results have demonstrated its superiority over other state-of-the-art deep-learning methods.

中文翻译：

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使用线性阵列换能器在光声计算机断层扫描中去除伪影的生成对抗网络。

凭借平衡的空间分辨率、穿透深度和成像速度，光声计算机断层扫描 (PACT) 有望用于临床转化，例如乳腺癌筛查、功能性脑成像和手术指导。PACT 通常使用线性超声 (US) 换能器阵列，对于手持应用具有极大的灵活性。然而，线性超声换能器阵列的检测角度范围和频率带宽有限，导致重建 PACT 图像中的视野受限和带宽受限。这些伪影显着降低了成像质量。为了解决这些问题，现有的解决方案通常必须付出系统复杂性、成本和/或成像速度的代价。这里，我们提出了一种基于深度学习的方法，该方法探索了带有梯度惩罚的 Wasserstein 生成对抗网络 (WGAN-GP)，以减少 PACT 中的受限视图和受限带宽伪影。与现有的重建和卷积神经网络方法相比，我们的模型在成像质量和分辨率方面都有所提高。我们在模拟、幻像和体内数据共同证明了应用 WGAN-GP 来提高 PACT 图像质量的可行性，而无需对当前的成像设置进行任何修改。

影响陈述

本研究具有以下主要影响。它为使用线性阵列换能器和传统图像重建消除 PACT 中的受限视图和受限带宽伪影提供了一种有前途的解决方案，这些伪影长期以来一直阻碍其临床转化。我们的解决方案显示出前所未有的体内图像伪​​影去除能力，这可能实现重要的应用，例如成像肿瘤血管生成和缺氧。该研究首次报告了使用基于稳定生成对抗网络的高级深度学习模型。我们的结果证明了它优于其他最先进的深度学习方法。