Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Hao ZhangZhengrong Liang

Abstract

To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially variant filtering parameters which are adaptive to the local characteristics of th...Continue Reading

References

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Aug 1, 2014·Journal of X-ray Science and Technology·Yan LiuZhengrong Liang

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Citations

Jun 13, 2016·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Robert Cierniak, Anna Lorent
Nov 7, 2015·Computer Methods and Programs in Biomedicine·Hong ShangguanZhiguo Gui
Jun 25, 2016·Journal of X-ray Science and Technology·Yuanyuan GaoWufan Chen
Feb 22, 2018·Journal of Digital Imaging·Xin Yi, Paul Babyn
Nov 7, 2019·Australasian Physical & Engineering Sciences in Medicine·Yining HuLimin Luo
Sep 29, 2018·Physics in Medicine and Biology·Yongbo WangJianhua Ma
Mar 18, 2021·Physics in Medicine and Biology·Hao ZhangLei Xing
Apr 29, 2021·Physics in Medicine and Biology·Sui LiJianhua Ma

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