Fan Q, Niu T, Zhu L
Nuclear & Radiological Engineering and Medical Physics Programs, Georgia Institute of Technology, Atlanta, GA.
Med Phys. 2012 Jun;39(6Part28):3974. doi: 10.1118/1.4736218.
Cone-beam CT (CBCT) images contain severe shading artifacts mostly due to scatter. Many algorithms have been proposed to alleviate this problem by data correction on projections. Sophisticated methods have also been designed to further improve the image quality when prior patient information is available. In this work, we develop a novel algorithm for shading correction directly on CBCT images without any prior information.
In CBCT, projection errors (mostly scatter) result in dominant low-frequency shading artifacts in image domain. Due to circular scan geometry, these artifacts often show global or local radial patterns. We first convert the uncorrected images into the polar coordinate system. Median filtering and polynomial fitting are applied on the transformed images to accurately estimate the low-frequency bias field of shading angle-by-angle and slice-by-slice. The estimated bias field is then converted back to the Cartesian coordinate system, followed by 3D low-pass filtering to eliminate possible high-frequency components. The shading-corrected images are finally obtained as the uncorrected volume divided by the shading bias field.
The proposed algorithm has been evaluated on CBCT images of a pelvis patient and a head patient. Within regions of interest, the average CBCT error is reduced from around 250 HU to 42 and 38 HU, and the spatial non-uniformity error is reduced from above 17.5% to 2.1% and 1.7% for the pelvis and the head patients, respectively. As our method suppresses only low-frequency shading artifacts, patient anatomy and contrast are well retained in the corrected images of both cases.
We propose an effective shading correction algorithm on CBCT images, with several advantages compared to existing approaches. The method has a high efficiency since it is deterministic and directly operates on the reconstructed images. It also requires no prior information, which facilitates its clinical use as a standard image correction solution.
锥束CT（CBCT）图像包含严重的阴影伪影，主要是由于散射造成的。已经提出了许多算法通过对投影进行数据校正来缓解这个问题。当有患者先验信息可用时，也设计了复杂的方法来进一步提高图像质量。在这项工作中，我们开发了一种无需任何先验信息即可直接对CBCT图像进行阴影校正的新算法。
在CBCT中，投影误差（主要是散射）在图像域中导致占主导地位的低频阴影伪影。由于圆形扫描几何结构，这些伪影通常呈现全局或局部径向模式。我们首先将未校正的图像转换到极坐标系。对变换后的图像应用中值滤波和多项式拟合，逐角度、逐切片地准确估计阴影的低频偏差场。然后将估计的偏差场转换回笛卡尔坐标系，接着进行三维低通滤波以消除可能的高频分量。最终通过将未校正的体数据除以阴影偏差场得到阴影校正后的图像。
所提出的算法已在一名骨盆患者和一名头部患者的CBCT图像上进行了评估。在感兴趣区域内，骨盆患者和头部患者的CBCT平均误差分别从约250 HU降至42 HU和38 HU，空间不均匀性误差分别从高于17.5%降至2.1%和1.7%。由于我们的方法仅抑制低频阴影伪影，在两种情况下的校正图像中患者解剖结构和对比度都得到了很好的保留。
我们提出了一种有效的CBCT图像阴影校正算法，与现有方法相比具有几个优点。该方法效率高，因为它是确定性的且直接对重建图像进行操作。它也不需要先验信息，这便于其作为标准图像校正解决方案在临床中使用。
