Computer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Siemens Healthineers AG, Advanced Therapies, Forchheim, Germany.
Int J Comput Assist Radiol Surg. 2019 Apr;14(4):601-610. doi: 10.1007/s11548-019-01912-6. Epub 2019 Feb 18.
The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts.
We introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images.
We have compared several corresponding regions of the associated real and simulated low-dose images-obtained from two different imaging systems-visually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regions-from 80 pairs of real and simulated low-dose regions-belonging to the same distribution has been accepted in 81.43% of the cases.
The results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.
X 射线图像的质量在计算机辅助介入中起着重要作用。虽然基于学习的去噪技术已被证明可以成功地提高图像质量，但它们通常依赖于一对相关的低剂量和高剂量 X 射线图像，而在临床情况下通常不可能在不同剂量水平下获得这些图像。此外，由于数据变化是基于学习的方法的一个重要要求，仅使用体模数据可能还不够。解决这个问题的一种可能性是从相关的高剂量图像中真实地模拟低剂量图像。
我们介绍了一种基于 X 射线图像形成模型的新型噪声模拟方法。该方法利用与低剂量和高剂量 X 射线图像采集相关的系统参数，如系统增益和电子噪声，来保持低剂量图像的图像噪声特征。
我们比较了来自两个不同成像系统的相关真实和模拟低剂量图像的几个对应区域，通过在 5%的显著水平下使用双样本 Kolmogorov-Smirnov 检验进行了视觉和统计比较。除了视觉上相似外，在 81.43%的情况下，假设来自 80 对真实和模拟低剂量区域的对应区域属于同一分布的假设得到了接受。
结果表明，使用所提出的方法获得的模拟低剂量图像几乎与真实低剂量图像无法区分。由于可以避免使用先前方法所需的广泛校准程序，因此可以轻松地适应不同的 X 射线成像系统。这反过来又导致潜在基于学习的方法的训练数据的多样性增加。
