Literature DB >> 33882480

A motion correction approach for oral and maxillofacial cone-beam CT imaging.

Tao Sun1, Reinhilde Jacobs2, Ruben Pauwels3, Elisabeth Tijskens2, Roger Fulton4,5, Johan Nuyts6.   

Abstract

Patient movement affects image quality in oral and maxillofacial cone-beam computed tomography imaging. While many efforts are made to minimize the possibility of motion during a scan, relatively little attention has been given to motion correction after acquisition. We propose a novel method which can improve the image quality after an oral and maxillofacial scan. The proposed method is based on our previous work and is a retrospective motion estimation and motion compensation (ME/MC) approach that iteratively estimates and compensates for rigid pose change over time. During motion estimation, image update and motion update are performed alternately in a multi-resolution scheme to obtain the motion. We propose use of a feature-based motion update and patch-based image update in the iterative estimation process, to alleviate the effect of limited scan field of view on estimation. During motion compensation, a fine-resolution image reconstruction was performed with compensation for the estimated motion. The proposed ME/MC method was evaluated with simulations, phantom and patient studies. Two experts in dentomaxillofacial radiology assessed the diagnostic importance of the resulting motion artifact suppression. The quality of the reconstructed images was improved after motion compensation, and most of the image artifacts were eliminated. Quantitative analysis by comparison to a reference image and by calculation of a sharpness metric agreed with the qualitative observation. The results are promising, and further evaluation is required to assess the clinical value of the proposed method.
© 2021 Institute of Physics and Engineering in Medicine.

Entities:  

Keywords:  cone-beam computed tomography (CBCT); motion compensation; motion estimation; oral and maxillofacial imaging

Mesh:

Year:  2021        PMID: 33882480     DOI: 10.1088/1361-6560/abfa38

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


  1 in total

1.  Inferring CT perfusion parameters and uncertainties using a Bayesian approach.

Authors:  Tao Sun; Roger Fulton; Zhanli Hu; Christina Sutiono; Dong Liang; Hairong Zheng
Journal:  Quant Imaging Med Surg       Date:  2022-01
  1 in total

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