Two-stage sparsity-regularized reconstruction for wide-angle DBT augmented with background estimation to improve depth resolution, in-plane contrast, and uniformity, demonstrated on one patient case.
Efficient primal-dual algorithm for imaging applications with matrix stacking, applied to DBT image reconstruction
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abstract
The primal-dual hybrid gradient (PDHG) algorithm for solving convex optimization problems that arise in tomographic imaging is revisited. In particular, simplification of the selection of step-size parameters is developed for optimization problems with multiple terms, each containing a linear transform subject to splitting. This simplification maintains algorithm efficiency while avoiding massive grid searches for the optimal parameter settings. The PDHG framework is demonstrated on an image reconstruction problem for wide-angle digital breast tomosythesis (DBT); use of the proposed optimization problem is enabled by the framework and it is demonstrated to have some advantage in quantitative accuracy of the reconstructed volume and in improving DBT depth resolution.
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math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Improving depth-resolution, in-plane contrast, and reducing non-uniformity artifacts for wide-angle DBT
Two-stage sparsity-regularized reconstruction for wide-angle DBT augmented with background estimation to improve depth resolution, in-plane contrast, and uniformity, demonstrated on one patient case.