Model Image-Based Metal Artifact Reduction for Computed Tomography
Model Image-Based Metal Artifact Reduction for Computed Tomography
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DOI：
10.1007/s10278-019-00210-6
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发表时间：
2020-02-01
影响因子：
4.4
通讯作者：
Wu, Jay
中科院分区：
文献类型：
--
作者：
Luzhbin, Dmytro;Wu, Jay
金属植入物往往会在重建的CT图像中产生严重的伪影，造成信息和图像细节的丢失，使CT图像无法诊断。为了消除金属伪影，提高重建CT图像的诊断价值，提出了一种基于阈值分割的组织类模型和具有空间信息的k-均值聚类的后处理金属伪影去除算法。该算法结合了图像修复技术，提高了对金属伪影严重污染的CT图像的分割精度。对一个水模和两组临床CT图像进行了研究，以测试算法的性能。该方法有效地消除了典型的金属伪影，将不同组织的平均CT数恢复到合适的水平，并保留了边缘和对比度信息，从而可以准确地重建组织衰减图。伪影校正后的CT图像的质量允许它们随后用于其他临床应用，如三维渲染、放射治疗的剂量估计、PET和SPECT的衰减校正等。该算法不依赖于原始正弦图的使用，因此不受专有格式限制。
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.