A feasibility study on the application of separable coded masks to X-ray fluorescence imaging,Journal of Analytical Atomic Spectrometry

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A feasibility study on the application of separable coded masks to X-ray fluorescence imaging
Journal of Analytical Atomic Spectrometry ( IF 3.1 ) Pub Date : 2020-11-21 , DOI: 10.1039/d0ja00413h
Shifeng Sun _{1,

2,

3,

4} , Xiaoping Ouyang _{4,

5,

6,

7}

Affiliation

For every imaging method, optics plays a vital role. Compared to polycapillary optics or a pinhole-collimator, the use of coded apertures as X-ray optics has the advantages of simple fabrication, high sensitivity, and scalability. Therefore, this work explores the feasibility of applying the coded aperture method to X-ray fluorescence imaging. The proposed imaging system consists of a 2D position-sensitive detector coupled to a 2D multi-hole mask, which is parallel and center-aligned to the detector. To reduce the complexity of system calibration and image reconstruction, a separable mask design and a novel near-field coded aperture imaging model were adapted. The performance of the system was investigated using the Geant4 Monte Carlo simulations. Image reconstruction was performed with the iterative algorithm and the deep learning neural network. High quality 2D and 3D images of complex shaped objects can be reconstructed from a single recorded coded image. Unlike imaging systems based on the conventional convolution model, this system can maintain high spatial resolution over a considerable distance range. For the object-to-mask distances of 8 mm and 26 mm, the spatial resolution is 23.7 μm and 36.2 μm, respectively. The 3D reconstruction results show that the system is able to correctly estimate the object-to-mask distance with an axial spatial resolution of 0.75 mm.

中文翻译：

可分离编码掩模在X射线荧光成像中应用的可行性研究

对于每种成像方法，光学起着至关重要的作用。与多毛细管光学器件或针孔准直器相比，编码孔径作为X射线光学器件的使用具有制造简单，灵敏度高和可扩展性的优点。因此，这项工作探索了将编码孔径方法应用于X射线荧光成像的可行性。所提出的成像系统包括一个二维位置敏感探测器，该探测器耦合到一个二维多孔掩模上，该掩模平行且中心对准探测器。为了减少系统校准和图像重建的复杂性，采用了可分离的掩模设计和新颖的近场编码孔径成像模型。使用Geant4 Monte Carlo模拟研究了系统的性能。使用迭代算法和深度学习神经网络进行图像重建。复杂形状对象的高质量2D和3D图像可以从单个记录的编码图像中重建。与基于常规卷积模型的成像系统不同，该系统可以在相当大的距离范围内保持较高的空间分辨率。对于8 mm和26 mm的对象到蒙版距离，空间分辨率分别为23.7μm和36.2μm。3D重建结果表明，该系统能够以0.75 mm的轴向空间分辨率正确估计对象到蒙版的距离。空间分辨率分别为23.7μm和36.2μm。3D重建结果表明，该系统能够以0.75 mm的轴向空间分辨率正确估计对象到蒙版的距离。空间分辨率分别为23.7μm和36.2μm。3D重建结果表明，该系统能够以0.75 mm的轴向空间分辨率正确估计对象到蒙版的距离。

更新日期：2020-12-09

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