Positron emission tomography (PET) image reconstruction from low-count projection data and physical effects is challenging because the inverse problem is ill-posed and the resultant image is usually noisy. Recently, generative adversarial networks (GANs) have also shown their superior performance in many computer vision tasks and attracted growing interests in medical imaging. In this work, the authors proposed a novel model [deep residual generative adversarial network (DRGAN)] based on GANs for the reduction of streaking artefacts and the improvement of PET image quality. An innovative feature of the proposed method is that the authors trained a generator to produce ‘residual PET map’ (RPM) for image representation, rather than generate PET images directly. DRGAN used two discriminators (critics) to enforce anatomically realistic PET images and RPM. To better boost the contextual information, the authors designed residual dense connections followed with pixel shuffle operations (RDPS blocks) that encourage feature reuse and prevent losing resolution. Both simulation data and real clinical PET data are used to evaluate the proposed method. Compared with other state-of-the-art methods, the quantification results show that DRGAN can achieve better performance in bias–variance trade-off and provide comparable image quality. Their results were rigorously evaluated by one radiologist at the Shanxi Cancer Hospital.

中文翻译：

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DRGAN：用于PET图像重建的深度残留生成对抗网络

利用低计数投影数据和物理效应重建正电子发射断层扫描（PET）图像非常具有挑战性，因为反问题不适当地适用，并且生成的图像通常很吵。最近，生成对抗网络（GAN）还显示了其在许多计算机视觉任务中的优越性能，并引起了人们对医学成像日益增长的兴趣。在这项工作中，作者提出了一种基于GAN的新型模型[深残留生成对抗网络（DRGAN）]，以减少条纹痕迹并改善PET图像质量。该方法的创新之处在于，作者训练了生成器以生成“残余PET图”（RPM）以进行图像表示，而不是直接生成PET图像。DRGAN使用了两个鉴别器（批评家）来增强解剖学上逼真的PET图像和RPM。为了更好地增强上下文信息，作者设计了剩余的密集连接，然后进行像素混洗操作（RDPS块），这些操作可鼓励要素重用并防止丢失分辨率。仿真数据和实际临床PET数据均用于评估该方法。与其他最新方法相比，量化结果表明DRGAN可以在偏差－方差折衷方案中实现更好的性能，并提供可比的图像质量。他们的结果由山西省肿瘤医院的一名放射科医生严格评估。作者设计了剩余的密集连接，然后进行像素混洗操作（RDPS块），这些操作可鼓励功能重用并防止分辨率降低。仿真数据和实际临床PET数据均用于评估该方法。与其他最新方法相比，量化结果表明DRGAN可以在偏差－方差折衷方案中实现更好的性能，并提供可比的图像质量。他们的结果由山西省肿瘤医院的一名放射科医生严格评估。作者设计了剩余的密集连接，然后进行像素混洗操作（RDPS块），这些操作可鼓励功能重用并防止分辨率降低。仿真数据和实际临床PET数据均用于评估该方法。与其他最新方法相比，量化结果表明DRGAN可以在偏差－方差折衷方案中实现更好的性能，并提供可比的图像质量。他们的结果由山西省肿瘤医院的一名放射科医生严格评估。