Name
Title | ||
|---|---|---|
Model-Based Iterative Tomographic Reconstruction With Adaptive Sparsifying Transforms |
Abstract | ||
|---|---|---|
Model based iterative reconstruction algorithms are capable of reconstructing high-quality images from low-dose CT measurements. The performance of these algorithms is dependent on the ability of a signal model to characterize signals of interest. Recent work has shown the promise of signal models that are learned directly from data. We propose a new method for low-dose tomographic reconstruction by combining adaptive sparsifying transform regularization within a statistically weighted constrained optimization problem. The new formulation removes the need to tune a regularization parameter. We propose an algorithm to solve this optimization problem, based on the Alternating Direction Method of Multipliers and FISTA proximal gradient algorithm. Numerical experiments on the FORBILD head phantom illustrate the utility of the new formulation and show that adaptive sparsifying transform regularization outperforms competing dictionary learning methods at speeds rivaling total-variation regularization. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/12.2041011 | COMPUTATIONAL IMAGING XII |
Keywords | Field | DocType |
Sparisfying transform learning, Sparse representations, CT dose reduction, Iterative reconstruction | Iterative reconstruction,Computer vision,Tomographic reconstruction,Imaging phantom,Transform theory,Tomography,Regularization (mathematics),Associative array,Artificial intelligence,Optimization problem,Physics | Conference |
Volume | ISSN | Citations |
9020 | 0277-786X | 9 |
PageRank | References | Authors |
0.49 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luke Pfister | 1 | 37 | 2.78 |
| Yoram Bresler | 2 | 1104 | 119.17 |