人工智能
非线性降维
工件(错误)
迭代重建
投影(关系代数)
成像体模
计算机视觉
计算机科学
歧管(流体力学)
医学影像学
还原(数学)
模式识别(心理学)
特征(语言学)
降维
数学
核医学
算法
医学
几何学
工程类
机械工程
语言学
哲学
作者
Junbo Peng,Chih‐Wei Chang,Huiqiao Xie,Mingdong Fan,Tonghe Wang,Justin Roper,Richard L. J. Qiu,Xiangyang Tang,Xiaofeng Yang
摘要
Computed tomography (CT) imaging is widely used for medical diagnosis and image guidance for treatment. Metal artifacts are observed on the reconstructed CT images if metal implants are carried by patients due to the beam hardening effects. In this condition, the acquired projection data cannot be used for analytical reconstruction as they do not meet Tuy's data sufficiency condition. Numerous deep learning-based methods have been developed for metal artifact reduction (MAR), providing superior performance. Nevertheless, all the reported models are data-driven and require large-size referenced images for the manifold approximation. In this work, we propose a physics-driven sinogram manifold learning method, which fully exploits the projection data correlation in CT scanning for MAR, and the proposed method is ready to be extended to other data-incomplete CT reconstruction problems.
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