Work

Efficient primal-dual algorithm for imaging applications with matrix stacking, applied to DBT image reconstruction

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

Authors: Sidky, Emil Y., Phillips, John Paul, Zhang, Zheng, Xia, Dan, Reiser, Ingrid S., Pan, Xiaochuan

Institution: University of Chicago

Language: English

Topic: Digital Radiography and Breast Imaging

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funder: National Institutes of Health

Award: Intraoperative quantitative CT imaging of breast specimen for reducing re-surgery rate and tumor cataloguing

OpenAlex ID: W7158423306

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