The application of ionizing radiation for diagnostic imaging is common around the globe. However, the process of imaging, itself, remains to be a relatively hazardous operation. Therefore, it is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems, where multiple x-ray operations are performed for the reconstruction of slices of body tissues. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique, which reduces the x-ray dose but can cause a loss of image sharpness. Since CT image reconstruction from directional x-rays is a nonlinear process, it is analytically difficult to correct the effect of dose reduction on image quality. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts. Some recent works propose combinations of multiple deep-learning and classical methods for this purpose, which over-complicate the process. However, it is observed here that the straight utilization of the well-known U-NET provides very successful results for the correction of low-dose artifacts. Blind tests with actual radiologists reveal that the U-NET enhanced quarter-dose CT images not only provide an immense visual improvement over the low-dose versions, but also become diagnostically preferable images, even when compared to their full-dose CT versions.

翻译：电离辐射诊断成像在全球范围内广泛应用。然而，成像过程本身仍是一项具有潜在危险的操作。因此，特别是在计算机断层扫描（CT）成像系统中，应尽可能使用低剂量的电离辐射。CT系统通过多次X射线操作完成人体组织切片的图像重建。CT成像中一种常用的辐射剂量降低方法称为四分之一剂量技术，该技术减少X射线剂量，但可能导致图像清晰度下降。由于基于定向X射线的CT图像重建是非线性过程，因此从分析角度难以校正剂量降低对图像质量的影响。近期流行的深度学习方法为低剂量伪影的图像增强提供了有趣的可能性。近期一些研究提出了结合多种深度学习与经典方法的方案，但使过程过于复杂化。然而，本研究发现直接使用经典的U-NET网络能够成功校正低剂量伪影。由资深放射科医师进行的盲测实验表明，经U-NET增强的四分之一剂量CT图像不仅相比低剂量版本在视觉上有显著提升，甚至在全剂量CT图像的对比中也更具诊断偏好性。