Literature DB >> 28585520

Iterative image-domain ring artifact removal in cone-beam CT.

Xiaokun Liang1, Zhicheng Zhang, Tianye Niu, Shaode Yu, Shibin Wu, Zhicheng Li, Huailing Zhang, Yaoqin Xie.   

Abstract

Ring artifacts in cone beam computed tomography (CBCT) images are caused by pixel gain variations using flat-panel detectors, and may lead to structured non-uniformities and deterioration of image quality. The purpose of this study is to propose a method of general ring artifact removal in CBCT images. This method is based on the polar coordinate system, where the ring artifacts manifest as stripe artifacts. Using relative total variation, the CBCT images are first smoothed to generate template images with fewer image details and ring artifacts. By subtracting the template images from the CBCT images, residual images with image details and ring artifacts are generated. As the ring artifact manifests as a stripe artifact in a polar coordinate system, the artifact image can be extracted by mean value from the residual image; the image details are generated by subtracting the artifact image from the residual image. Finally, the image details are compensated to the template image to generate the corrected images. The proposed framework is iterated until the differences in the extracted ring artifacts are minimized. We use a 3D Shepp-Logan phantom, Catphan©504 phantom, uniform acrylic cylinder, and images from a head patient to evaluate the proposed method. In the experiments using simulated data, the spatial uniformity is increased by 1.68 times and the structural similarity index is increased from 87.12% to 95.50% using the proposed method. In the experiment using clinical data, our method shows high efficiency in ring artifact removal while preserving the image structure and detail. The iterative approach we propose for ring artifact removal in cone-beam CT is practical and attractive for CBCT guided radiation therapy.

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Year:  2017        PMID: 28585520     DOI: 10.1088/1361-6560/aa7017

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  9 in total

1.  A new iterative algorithm for ring artifact reduction in CT using ring total variation.

Authors:  Morteza Salehjahromi; Qian Wang; Yanbo Zhang; Lars A Gjesteby; Dan Harrison; Ge Wang; Peter M Edic; Hengyong Yu
Journal:  Med Phys       Date:  2019-09-20       Impact factor: 4.071

2.  Automated cardiovascular risk categorization through AI-driven coronary calcium quantification in cardiac PET acquired attenuation correction CT.

Authors:  S G M van Velzen; M M Dobrolinska; P Knaapen; R L M van Herten; R Jukema; I Danad; R H J A Slart; M J W Greuter; I Išgum
Journal:  J Nucl Cardiol       Date:  2022-07-18       Impact factor: 3.872

3.  Sparsity-based method for ring artifact elimination in computed tomography.

Authors:  Mona Selim; Essam A Rashed; Mohammed A Atiea; Hiroyuki Kudo
Journal:  PLoS One       Date:  2022-06-28       Impact factor: 3.752

4.  Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.

Authors:  Xiaokun Liang; Na Li; Zhicheng Zhang; Shaode Yu; Wenjian Qin; Yafen Li; Shupeng Chen; Huailing Zhang; Yaoqin Xie
Journal:  Quant Imaging Med Surg       Date:  2019-07

5.  Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D).

Authors:  Shaode Yu; Shibin Wu; Ling Zhuang; Xinhua Wei; Mark Sak; Duric Neb; Jiani Hu; Yaoqin Xie
Journal:  Sensors (Basel)       Date:  2017-08-08       Impact factor: 3.576

6.  Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems.

Authors:  Jakub Šalplachta; Tomáš Zikmund; Marek Zemek; Adam Břínek; Yoshihiro Takeda; Kazuhiko Omote; Jozef Kaiser
Journal:  Sensors (Basel)       Date:  2021-01-01       Impact factor: 3.576

7.  Detection of Collaterals from Cone-Beam CT Images in Stroke.

Authors:  Azrina Abd Aziz; Lila Iznita Izhar; Vijanth Sagayan Asirvadam; Tong Boon Tang; Azimah Ajam; Zaid Omar; Sobri Muda
Journal:  Sensors (Basel)       Date:  2021-12-03       Impact factor: 3.576

8.  A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images.

Authors:  Shaode Yu; Guangzhe Dai; Zhaoyang Wang; Leida Li; Xinhua Wei; Yaoqin Xie
Journal:  BMC Med Imaging       Date:  2018-05-16       Impact factor: 1.930

9.  A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.

Authors:  Guoya Dong; Chenglong Zhang; Xiaokun Liang; Lei Deng; Yulin Zhu; Xuanyu Zhu; Xuanru Zhou; Liming Song; Xiang Zhao; Yaoqin Xie
Journal:  Front Oncol       Date:  2021-07-16       Impact factor: 6.244

  9 in total

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