X-ray imaging methods are used in many fields of research, as they allow a non-destructive investigation of the elemental content of various samples. As for every imaging method, for X-ray imaging the optics are of crucial importance. However, these optics can be very expensive and laborious to build, as the requirements on surface roughness and precision are extremely high. Angles of reflection and refraction are often in the range of a few mrad, making a compact design hard to achieve. In this work we present a possibility to simplify X-ray imaging. We have adapted the coded aperture method, a high energy radiation imaging method that has its origins in astrophysics, to full field X-ray fluorescence imaging. In coded aperture imaging, an object is projected through a known mask, the coded aperture, onto an area sensitive detector. The resulting image consists of overlapping projections of the object and a reconstruction step is necessary to obtain the information from the recorded image. We recorded fluorescence images of different samples with an energy-dispersive 2D detector (pnCCD) and investigated different reconstruction methods. With a small coded aperture with 12 holes we could significantly increase the count rate compared to measurements with a straight polycapillary optic. We show that the reconstruction of two different samples is possible with a deconvolution approach, an iterative algorithm and a neural network. These results demonstrate that X-ray fluorescence imaging with coded apertures has the potential to deliver good results without scanning and with an improved count rate, so that measurement times can be shortened compared to established methods.

中文翻译：

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基于反卷积，迭代算法和神经网络的三种编码孔径光学X射线荧光成像重建方法的比较

X射线成像方法用于许多研究领域，因为它们可以对各种样品的元素含量进行无损研究。对于每种成像方法，对于X射线成像，光学至关重要。但是，由于对表面粗糙度和精度的要求非常高，因此这些光学器件的制造可能非常昂贵且费力。反射角和折射角通常在几毫弧度的范围内，这使得很难实现紧凑的设计。在这项工作中，我们提出了简化X射线成像的可能性。我们已经将编码孔径方法（一种起源于天体物理学的高能辐射成像方法）改编为全场X射线荧光成像。在编码孔径成像中，物体通过已知的掩模（编码孔径）投射到区域敏感检测器上。生成的图像由对象的重叠投影组成，需要进行重构步骤才能从记录的图像中获取信息。我们使用能量色散二维检测器（pnCCD）记录了不同样品的荧光图像，并研究了不同的重建方法。与12孔小编码孔径相比，与使用直形多毛细管光学器件进行测量相比，我们可以显着提高计数率。我们表明，使用反卷积方法，迭代算法和神经网络可以重构两个不同的样本。这些结果表明，具有编码孔径的X射线荧光成像可以在不进行扫描的情况下以良好的计数率提供良好的结果，因此与已建立的方法相比，可以缩短测量时间。