Image-domain shading correction for cone-beam CT without prior patient information.
Image-domain shading correction for cone-beam CT without prior patient information.
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DOI：
10.1120/jacmp.v16i6.5424
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发表时间：
2015-11-08
影响因子：
2.1
通讯作者：
Zhu L
中科院分区：
文献类型：
--
作者：
Fan Q;Lu B;Park JC;Niu T;Li JG;Liu C;Zhu L
在高精度放射治疗时代，锥形束CT（CBCT）经常用于机载治疗指导。然而，CBCT图像通常包含严重的阴影伪影，这是由于大体积照明产生的强光子散射和非优化的患者特定数据测量，限制了CBCT的全面临床应用。已经提出了许多算法，以减轻这个问题的数据校正投影。当可以获得先前的患者信息时，还设计了复杂的方法。然而，一个标准的，高效的，有效的方法与大的适用性仍然是目前的临床实践难以捉摸。在这项工作中，我们开发了一种新的算法，直接对CBCT图像的阴影校正。与其他图像域校正方法不同，我们的方法不依赖于先前的患者信息或患者数据的先前假设。在CBCT中，投影误差（主要来自蝴蝶结滤波器的散射和非理想使用）导致图像域中占主导地位的低频阴影伪影。在圆形扫描几何结构中，这些伪影通常显示全局或局部径向图案。因此，首先将原始CBCT图像预处理到极坐标系中。中值滤波和多项式拟合应用于变换后的图像，以逐角度和逐切片估计低频阴影伪影（称为偏置场）。低通滤波过程首先沿角度方向沿着进行，然后沿径向进行，以保持图像对比度。然后将估计的偏置场转换回笛卡尔坐标系，然后进行3D低通滤波以消除可能的高频分量。阴影校正图像最终获得为未校正体积除以偏置场。所提出的算法进行了评价CBCT图像的骨盆患者和头部患者。将重建图像上的平均CT数值和空间非均匀性用作图像质量指标。在选定的感兴趣区域内，平均CT数误差从约300 HU降至42和38 HU，空间不均匀性误差从17.5%以上降至2.1%和1.7%，分别为骨盆和头部患者。由于我们的方法仅抑制低频阴影伪影，因此两种情况下的校正图像均保留了患者解剖结构和对比度。我们的CBCT图像阴影校正算法提供了几个优点。它具有很高的效率，因为它是确定性的，并直接对重建图像进行操作。它不需要先验信息或假设，不仅通过保留患者解剖结构实现了基于CBCT的治疗监测的优点，而且还促进了其作为有效图像校正解决方案的临床使用。PACS编号：87.57.C ‐、87.57.cp、87.57.Q ‐
In the era of high‐precision radiotherapy, cone‐beam CT (CBCT) is frequently utilized for on‐board treatment guidance. However, CBCT images usually contain severe shading artifacts due to strong photon scatter from illumination of a large volume and non‐optimized patient‐specific data measurements, limiting the full clinical applications of CBCT. Many algorithms have been proposed to alleviate this problem by data correction on projections. Sophisticated methods have also been designed when prior patient information is available. Nevertheless, a standard, efficient, and effective approach with large applicability remains elusive for current clinical practice. In this work, we develop a novel algorithm for shading correction directly on CBCT images. Distinct from other image‐domain correction methods, our approach does not rely on prior patient information or prior assumption of patient data. In CBCT, projection errors (mostly from scatter and non‐ideal usage of bowtie filter) result in dominant low‐frequency shading artifacts in image domain. In circular scan geometry, these artifacts often show global or local radial patterns. Hence, the raw CBCT images are first preprocessed into the polar coordinate system. Median filtering and polynomial fitting are applied on the transformed image to estimate the low‐frequency shading artifacts (referred to as the bias field) angle‐by‐angle and slice‐by‐slice. The low‐pass filtering process is done firstly along the angular direction and then the radial direction to preserve image contrast. 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 image is finally obtained as the uncorrected volume divided by the bias field. The proposed algorithm was evaluated on CBCT images of a pelvis patient and a head patient. Mean CT number values and spatial non‐uniformity on the reconstructed images were used as image quality metrics. Within selected regions of interest, the average CT number error was reduced from around 300 HU to 42 and 38 HU, and the spatial nonuniformity error was 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 were retained in the corrected images for both cases. Our shading correction algorithm on CBCT images offers several advantages. It has a high efficiency, since it is deterministic and directly operates on the reconstructed images. It requires no prior information or assumptions, which not only achieves the merits of CBCT‐based treatment monitoring by retaining the patient anatomy, but also facilitates its clinical use as an efficient image‐correction solution. PACS number(s): 87.57.C‐, 87.57.cp, 87.57.Q‐
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影响因子：
3.5
作者：
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通讯作者：
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