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Shading correction for grating-based differential phase contrast X-ray imaging

机译:基于光栅的差分相衬X射线成像的阴影校正

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Grating-based differential phase-contrast X-ray is a novel imaging modality with excellent soft-tissue contrast. Besides standard X-ray attenuation, it provides complementary information on the differential phase shift and the dark-field signal, which reveals structure variations at (sub-)micron scale. Current experimental setups suffer from a narrow field of view of 2-4cm. Thus, multiple exposures have to be stitched together to image larger objects. However, individual exposures are inherently affected by intensity variations, such that tiling artifacts corrupt the stitched projection. These artifacts are most severe in the differential phase image and highly impact their diagnostic value. To address this issue, we propose a novel optimization-based algorithm for fully compensating these tiling artifacts. Our algorithm estimates a smooth bias field for each individual exposure with a global objective function that minimizes the intensity distortion within and across different tiles in the projection. Compared to a currently widely used heuristic, our algorithm leverages the information available from all exposures to estimate the individual bias fields. The evaluation shows the superiority of the proposed algorithm, as it produces bias-free images. To our knowledge, this is the first bias correction algorithm for differential phase images that yields images with nearly imperceptible transitions between individual exposures.
机译:基于光栅的差分相衬X射线是一种新型的成像方式,具有出色的软组织对比度。除了标准的X射线衰减之外,它还提供有关差分相移和暗场信号的补充信息,从而揭示了(亚)微米级的结构变化。当前的实验装置具有2-4cm的狭窄视场。因此,必须将多次曝光缝合在一起以对较大的物体成像。但是,单个的曝光会固有地受到强度变化的影响,例如,平铺伪影会破坏缝合的投影。这些伪影在差分相位图像中最为严重,并严重影响其诊断价值。为了解决这个问题,我们提出了一种新颖的基于优化的算法来完全补偿这些平铺伪像。我们的算法使用全局目标函数估算每个单独曝光的平滑偏差场,该函数将投影中不同图块内部和之间的强度失真最小化。与当前广泛使用的启发式算法相比,我们的算法利用了所有风险暴露中的可用信息来估计各个偏差字段。评估显示了所提出算法的优越性,因为它可以产生无偏差图像。据我们所知,这是用于差分相位图像的第一个偏差校正算法,该算法产生的图像在各个曝光之间具有几乎不可察觉的过渡。

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