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Use of a CMOS-based micro-CT system to validate a ring artifact correction algorithm on low-dose image data

机译:使用基于CMOS的微型CT系统验证低剂量图像数据上的环形伪影校正算法

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摘要

The imaging of objects using high-resolution detectors coupled to CT systems may be made challenging due to the presence of ring artifacts in the reconstructed data. Not only are the artifacts qualitatilvely distracting, they reduce the SNR of the reconstructed data and may lead to a reduction in the clinical utility of the image data. To address these challenges, we introduce a multistep algorithm that greatly reduces the impact of the ring artifacts on the reconstructed data through image processing in the sinogram space. First, for a single row of detectors corresponding to one slice, we compute the mean of every detector element in the row across all projection view angles and place the reciprocal values in a vector with length equal to the number of detector elements in a row. This vector is then multiplied with each detector element value for each projection view angle, obtaining a normalized or corrected sinogram. This sinogram is subtracted from the original uncorrected sinogram of the slice to obtain a difference map, which is then blurred with a median filter along the row direction. This blurred difference map is summed back to the corrected sinogram, to obtain the final sinogram, which can be back projected to obtain an axial slice of the scanned object, with a greatly reduced presence of ring artifacts. This process is done for each detector row corresponding to each slice. The performance of this algorithm was assessed using images of a mouse femur. These images were acquired using a micro-CT system coupled to a high-resolution CMOS detector. We found that the use of this algorithm led to an increase in SNR and a more uniform line-profile, as a result of the reduction in the presence of the ring artifacts.
机译:由于在重建数据中存在环形伪像,使用耦合到CT系统的高分辨率检测器对物体进行成像可能具有挑战性。伪像不仅在质量上分散注意力,而且它们降低了重建数据的SNR,并且可能导致图像数据的临床效用降低。为了解决这些挑战,我们引入了一种多步算法,该算法通过正弦图空间中的图像处理大大减少了环状伪影对重建数据的影响。首先,对于对应于一个切片的单排检测器,我们计算所有投影视角中该行中每个检测器元素的均值,并将倒数值放在长度等于一行中检测器元素数量的向量中。然后将此向量与每个投影视角的每个检测器元素值相乘,以获得归一化或校正后的正弦图。从切片的原始未经校正的正弦图中减去此正弦图,以获得差异图,然后使用行方向的中值滤波器对其进行模糊处理。将此模糊的差异图求和回校正的正弦图,以获得最终的正弦图,可以对最终的正弦图进行反投影,以获取扫描对象的轴向切片,并且大大减少了环状伪影。针对与每个切片相对应的每个检测器行执行此过程。使用鼠标股骨的图像评估了该算法的性能。这些图像是使用与高分辨率CMOS检测器耦合的micro-CT系统获取的。我们发现,由于减少了环状伪影,该算法的使用导致了SNR的提高和更均匀的线轮廓。

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