Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a post-processing metal artifact reduction algorithm, based on a tissue-class model segmented by thresholding and k-means clustering with spatial information, is proposed. The image inpainting technique is incorporated into the algorithm to improve the segmentation accuracy for CT images severely corrupted by metal artifacts. A study of a water phantom and of two sets of clinical CT images was performed to test the algorithm performance. The proposed method effectively eliminates typical metal artifacts, restores the average CT numbers of different tissues to the proper levels, and preserves the edge and contrast information, thus allowing the accurate reconstruction of the tissue attenuation map. The quality of the artifact-corrected CT images allows them to be subsequently used in other clinical applications, such as three-dimensional rendering, dose estimation for radiotherapy, attenuation correction for PET and SPECT, etc. The algorithm does not rely on the use of the raw sinogram and so is not limited by the proprietary format restrictions.

中文翻译：

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用于计算机断层扫描的基于模型图像的金属伪像减少。

金属植入物通常会在重建的计算机断层扫描（CT）图像中产生严重的伪影，从而导致信息和图像细节丢失，并使CT图像在诊断上无法使用。为了消除金属伪影并提高重建CT图像的诊断价值，提出了一种基于组织类模型的后处理金属伪影减少算法，该模型通过阈值分割和具有空间信息的k均值聚类。该算法中加入了图像修复技术，以提高对因金属伪影严重损坏的CT图像的分割精度。对水体模和两组临床CT图像进行了研究，以测试算法的性能。所提出的方法有效地消除了典型的金属工件，将不同组织的平均CT数恢复到适当的水平，并保留边缘和对比度信息，从而可以准确地重建组织衰减图。伪影校正的CT图像的质量允许它们随后用于其他临床应用，例如三维渲染，放射疗法的剂量估算，PET和SPECT的衰减校正等。该算法不依赖于使用原始正弦图，因此不受专有格式限制的限制。