A Comparative Study of Neighborhood Filters for Artifact Reduction in Iterative Low-Dose CT

Wei Xu, Sungsoo Ha, Ziyi Zheng, Klaus Mueller
Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11777 USA
12th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013
@article{xu2013comparative,

   title={A Comparative Study of Neighborhood Filters for Artifact Reduction in Iterative Low-Dose CT},

   author={Xu, Wei and Ha, Sungsoo and Zheng, Ziyi and Mueller, Klaus},

   year={2013}

}

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Iterative CT algorithms have become increasingly popular in recent years. They have been found useful when the projections are limited in number, irregularly spaced, or noisy, which are often encountered in low-dose CT imaging. One way to cope with the associated streak and noise artifacts is to interleave a regularization objective into the iterative reconstruction framework. In this paper we investigate a number of non-linear neighborhood filters within an iterative CT framework, OS-SIRT, and compare them with total variation minimization (TVM). We find that the Non-Local Means (NLM) filter provides the best performance, in particular its patch-based variant. Further, we also compare a scheme that exploits an artifact-free reference image for even better regularization performance. Finally, we also compare the studied filters in terms of their computational efficiency with acceleration on modern GPUs.
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