计算机科学
工件(错误)
人工智能
投影(关系代数)
计算机视觉
还原(数学)
领域(数学分析)
计算机断层摄影术
图像(数学)
模式识别(心理学)
算法
放射科
几何学
数学
医学
数学分析
作者
Yuanyuan Lyu,Wei-An Lin,Haofu Liao,Jingjing Lu,S. Kevin Zhou
标识
DOI:10.1007/978-3-030-59713-9_15
摘要
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. However, they are inherently local in the sinogram domain. Thus, one possible approach to MAR is to exploit the latter characteristic by learning to reduce artifacts in the sinogram. However, if we directly treat the metal-affected regions in sinogram as missing and replace them with the surrogate data generated by a neural network, the artifact-reduced CT images tend to be over-smoothed and distorted since fine-grained details within the metal-affected regions are completely ignored. In this work, we provide analytical investigation to the issue and propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection such that the geometry information of metal implants is encoded. Extensive experiments on simulated datasets and expert evaluations on clinical images demonstrate that our novel network yields anatomically more precise artifact-reduced images than the state-of-the-art approaches, especially when metallic objects are large.
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