Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/42.538943 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
image quality,iteration number,dental phantoms,computerised tomography,iterative deblurring,expectation maximization formulation,euclidean distance,i-divergence,filtered backprojection,medical diagnostic imaging,medical computed tomography,algebraic reconstruction technique,image reconstruction,computational complexity,synthetic noise-free data,metal artifact reduction,algorithm theory,reprojection,backprojection,ct metal artifact reduction,iterative methods,medical image processing,additive noisy projection data,expectation maximization,maximization,image processing,accuracy,reduction,algorithms,expectation,iterative algorithm,dentistry,biomedical imaging,computed tomography,metal | Iterative reconstruction,Computer vision,Mathematical optimization,Deblurring,Iterative method,Expectation–maximization algorithm,Euclidean distance,Image processing,Image quality,Algebraic Reconstruction Technique,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
15 | 5 | 0278-0062 |
Citations | PageRank | References |
47 | 4.96 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ge Wang | 1 | 1000 | 142.51 |
| D. Snyder | 2 | 112 | 40.42 |
| J A O'Sullivan | 3 | 47 | 4.96 |
| Michael W. Vannier | 4 | 1380 | 360.85 |