Iterative deblurring for CT metal artifact reduction

• Published in: IEEE transactions on medical imaging Vol. 15; no. 5; pp. 657 - 664
• Main Authors: Ge Wang, Snyder, D.L., O'Sullivan, J.A., Vannier, M.W.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-10-1996; Institute of Electrical and Electronics Engineers
• Subjects: Additive noise; Biological and medical sciences; Biomedical imaging; Computed tomography; Dentistry; Image quality; Image reconstruction; Imaging phantoms; Investigative techniques, diagnostic techniques (general aspects); Iterative algorithms; Iterative methods; Medical sciences; Miscellaneous. Technology; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Subspace constraints; Reduction; Maximization; Radiodiagnosis; X ray; Computerized axial tomography; Metal; Expectation
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.538943

Iterative deblurring methods using the expectation maximization (EM) formulation and the algebraic reconstruction technique (ART), respectively, are adapted for metal artifact reduction in medical computed tomography (CT). In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps. Furthermore, the EM-type algorithm converges faster than the ART-type algorithm in terms of either the I-divergence or Euclidean distance between ideal and reprojected data in the authors' simulation. Also, for a given iteration number, the EM-type deblurring method produces better image clarity but stronger noise than the ART-type reconstruction. The computational complexity of EM- and ART-based iterative deblurring is essentially the same, dominated by reprojection and backprojection. Relevant practical and theoretical issues are discussed.