Pattern Recognition Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
International Max Planck Research School - Physics of Light, Erlangen, Germany.
Int J Comput Assist Radiol Surg. 2021 Dec;16(12):2099-2106. doi: 10.1007/s11548-021-02487-x. Epub 2021 Sep 9.
In Talbot-Lau X-ray phase contrast imaging, the measured phase value depends on the position of the object in the measurement setup. When imaging large objects, this may lead to inhomogeneous phase contributions within the object. These inhomogeneities introduce artifacts in tomographic reconstructions of the object.
In this work, we compare recently proposed approaches to correct such reconstruction artifacts. We compare an iterative reconstruction algorithm, a known operator network and a U-net. The methods are qualitatively and quantitatively compared on the Shepp-Logan phantom and on the anatomy of a human abdomen. We also perform a dedicated experiment on the noise behavior of the methods.
All methods were able to reduce the specific artifacts in the reconstructions for the simulated and virtual real anatomy data. The results show method-specific residual errors that are indicative for the inherently different correction approaches. While all methods were able to correct the artifacts, we report a different noise behavior.
The iterative reconstruction performs very well, but at the cost of a high runtime. The known operator network shows consistently a very competitive performance. The U-net performs slightly worse, but has the benefit that it is a general-purpose network that does not require special application knowledge.
在 Talbot-Lau X 射线相衬成像中，测量的相位值取决于物体在测量设置中的位置。当对大物体进行成像时，这可能会导致物体内部的相位贡献不均匀。这些不均匀性会在物体的层析重建中引入伪影。
在这项工作中，我们比较了最近提出的校正这些重建伪影的方法。我们比较了一种迭代重建算法、一个已知的算子网络和一个 U 形网络。在 Shepp-Logan 体模和人体腹部解剖结构上，对这些方法进行了定性和定量的比较。我们还对方法的噪声行为进行了专门的实验。
所有方法都能够减少模拟和虚拟真实解剖数据重建中特定的伪影。结果表明，方法特有的残余误差表明了固有的不同校正方法。虽然所有方法都能够校正伪影，但我们报告了不同的噪声行为。
迭代重建的性能非常好，但代价是运行时间长。已知的算子网络始终表现出非常有竞争力的性能。U 形网络的性能略差，但它是一个通用网络，不需要特殊的应用知识。
