Name
Abstract | ||
|---|---|---|
In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that relies on direct estimation of confidence intervals for each reconstructed value. Then, the second step uses confidence intervals as a constraint to choose the most appropriate candidate according to a regularization criterion. Two different constraints are proposed in this paper. The first one has the main advantage of strictly ensuring that the regularized solution will respect the interval-valued data-fitting constraint, thus preventing over-smoothing of the solution while offering interesting properties in terms of spatial and statistical bias/variance trade-off. Another regularization proposition based on the design of a smoother constraint also with appealing properties is proposed as an alternative. The competitiveness of the proposed framework is illustrated in comparison to other regularization schemes using analytical and GATE-based simulation and real PET acquisition. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2018.2886431 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
Image reconstruction,Positron emission tomography,TV,Kernel,Computational modeling,Interpolation,Estimation | Kernel (linear algebra),Iterative reconstruction,Mathematical optimization,Tomographic reconstruction,Scalar (physics),Interpolation,Regularization (mathematics),Quantization (signal processing),Confidence interval,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 6 | 1558-254X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F Kucharczak | 1 | 0 | 0.34 |
| F. Ben Bouallegue | 2 | 1 | 1.50 |
| O. Strauss | 3 | 153 | 21.17 |
| Denis Mariano-Goulart | 4 | 6 | 3.92 |