Division of X-Ray Imaging and CT, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.
Department of Physics and Astronomy, Ruprecht-Karls-University Heidelberg, Heidelberg, 69120, Germany.
Med Phys. 2021 Jul;48(7):3572-3582. doi: 10.1002/mp.14931. Epub 2021 May 28.
Metal artifacts can drastically reduce the diagnostic value of computed tomography (CT) images. Even the state-of-the-art algorithms cannot remove them completely. Photon-counting CT inherently provides spectral information, similar to dual-energy CT. Many applications, such as material decomposition, are not possible when metal artifacts are present. Our aim is to develop a prior-based metal artifact reduction specifically for photon-counting CT that can correct each bin image individually or in their combinations.
Photon-counting CT sorts incoming photons into several energy bins, producing bin and threshold images containing spectral information. We use this spectral information to obtain a better prior image for the state-of-the-art metal artifact reduction algorithm FSNMAR. First, we apply a non-linear transformation to the bin images to obtain bone-emphasized images. Subsequently, we forward-project the bin images and bone-emphasized images and multiply the resulting sinograms with each other element-wise to mimic beam hardening effects. These sinograms are reconstructed and linearly combined to produce an artifact-reduced image. The coefficients of this linear combination are automatically determined by minimizing a threshold-based cost function in the image domain. After thresholding, we obtain the prior image for FSNMAR, which is applied to the individual bin images and the lowest threshold image. We test our photon-counting normalized metal artifact reduction (PCNMAR) on forensic CT data and compare it to conventional FSNMAR, where the prior is generated via linear sinogram inpainting. For numerical analysis, we compute both the standard deviation in an ROI with metal artifacts and the CNR of soft tissue and fat.
PCNMAR can effectively reduce metal artifacts without sacrificing the overall image quality. Compared to FSNMAR, our method produces fewer secondary artifacts and is more consistent with the measurements. Areas that contain metal, air, and soft tissue are more accurate in PCNMAR. In some cases, the standard deviation in the artifact ROI is reduced by more than 50% relative to FSNMAR, while the CNR values are similar. If extreme artifacts are present, PCNMAR is unable to outperform FSNMAR. Using either two, four, or only the highest energy bin to produce the prior image yielded comparable results.
PCNMAR is an effective method of reducing metal artifacts in photon-counting CT. The spectral information available in photon-counting CT is highly beneficial for metal artifact reduction, especially the high-energy bin, which inherently contains fewer artifacts. While scanning with four instead of two bins does not provide a better artifact reduction, it allows for more freedom in the selection of energy thresholds.
金属伪影会极大地降低计算机断层扫描（CT）图像的诊断价值。即使是最先进的算法也无法完全去除它们。光子计数 CT 本质上提供了类似于双能 CT 的光谱信息。当存在金属伪影时，许多应用（如材料分解）是不可能的。我们的目标是开发一种专门针对光子计数 CT 的基于先验的金属伪影减少方法，该方法可以单独或组合地纠正每个 bin 图像。
光子计数 CT 将入射光子分成多个能量 bin，生成包含光谱信息的 bin 和阈值图像。我们利用这些光谱信息为最先进的金属伪影减少算法 FSNMAR 获得更好的先验图像。首先，我们对 bin 图像应用非线性变换以获得强调骨骼的图像。随后，我们正向投影 bin 图像和强调骨骼的图像，并逐个元素相乘以模拟束硬化效应。这些 sinogram 被重建并线性组合以生成一个减少伪影的图像。这个线性组合的系数是通过在图像域中最小化基于阈值的代价函数自动确定的。阈值化后，我们获得了用于 FSNMAR 的先验图像，该图像应用于单个 bin 图像和最低阈值图像。我们在法医 CT 数据上测试了我们的光子计数归一化金属伪影减少（PCNMAR），并将其与传统的 FSNMAR 进行了比较，其中先验是通过线性 sinogram 修复生成的。为了进行数值分析，我们计算了带有金属伪影的 ROI 中的标准差和软组织和脂肪的 CNR。
PCNMAR 可以有效地减少金属伪影，而不会牺牲整体图像质量。与 FSNMAR 相比，我们的方法产生的二次伪影更少，并且与测量结果更一致。包含金属、空气和软组织的区域在 PCNMAR 中更准确。在某些情况下，与 FSNMAR 相比，伪影 ROI 中的标准差降低了 50%以上，而 CNR 值相似。如果存在极端伪影，PCNMAR 无法超过 FSNMAR。使用两个、四个或仅最高能量 bin 生成先验图像产生了可比的结果。
PCNMAR 是一种减少光子计数 CT 中金属伪影的有效方法。光子计数 CT 中可用的光谱信息对金属伪影减少非常有益，特别是高能 bin，它本质上包含更少的伪影。虽然用四个而不是两个 bin 扫描并不能提供更好的伪影减少，但它允许在能量阈值的选择上有更多的自由。
