Ordered subsets Non-Local means constrained reconstruction for sparse view cone beam CT system

  • Yining HuEmail author
  • Zheng Wang
  • Lizhe XieEmail author
  • Limin Luo
Scientific Paper


Sparse-view sampling scans reduce the patient's radiation dose by reducing the total exposure duration. CT reconstructions under such scan mode are often accompanied by severe artifacts due to the high ill-posedness of the problem. In this paper, we use a Non-Local means kernel as a regularization constraint to reconstruct image volumes from sparse-angle sampled cone-beam CT scans. To overcome the huge computational cost of the 3D reconstruction, we propose a sequential update scheme relying on ordered subsets in the image domain. It is shown through experiments on simulated and real data and comparisons with other methods that the proposed approach is robust enough to deal with the number of views reduced up to 1/10. When coupled with a CUDA parallel computing technique, the computation speed of the iterative reconstruction is greatly improved.


Image reconstruction Tomography Cone beam CT Sparse view Low dose 



The authors are indebted to Dr. Jean-Louis Coatrieux, University of Rennes 1, Inserm U1099, Rennes, France, for his contributions in conducting this work. They thank Prof. Jianhua Ma from Southern Medical University, Guangzhou, China, for providing the experimental data. They also thank Prof. Jinglu Zhang from Nanjing Medical University, Nanjing, China, for her assistance in completing the experiments. This project has been supported by the National Natural Science Foundation of China under Grant No. 81530060; Basic Research Program of Jiangsu Province under Grant (BK20180670); Open Project from Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University (JSKLOD-KF-1701, JSKLOD-KF-1708); Open Project from Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China (K93-9–2014-10C); Science and Technology Plan of Nanjing (201715017).

Compliance with ethical standards

Conflict of interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.

Ethical approval

All applicable international, national, and institutional guidelines for the care and use of animals were followed.


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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.School of Cyber Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Computer Network and Information Integration (Southeast University)Ministry of EducationNanjingChina
  3. 3.Institute of StomatologyNanjing Medical UniversityNanjingChina
  4. 4.Jiangsu Key Laboratory of Oral DiseasesNanjing Medical UniversityNanjingChina
  5. 5.Centre de Recherche en Information BiomédicaleSino-Francais (LIA CRIBs)RennesFrance

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