arXiv subjects

John Paul Phillips

Publications and source records attributed to John Paul Phillips.

4 recordsLinked to original sources

arXiv2026

Improving depth-resolution, in-plane contrast, and reducing non-uniformity artifacts for wide-angle DBT

Purpose: This work aims to develop an image reconstruction algorithm for wide-angle digital breast tomosynthesis (DBT) that has improved depth resolution and in-plane contrast while reducing non-uniformity artifacts. Approach: The image reconstruction algorithm is an extension of our prior work on sparsity-regularized iterative image reconstruction. The algorithm is performed in two stages as explained in a prior work. The first stage consists of a low-resolution reconstruction that exploits sparsity for quantitative accuracy. In this work, this first stage is augmented with a formulation that includes the estimation of a "background" image, which absorbs low-frequency artifacts that cause image non-uniformity. Results: The new algorithm is demonstrated on a patient case for which the data are acquired on a wide-angle DBT system. Conclusion: The results on the shown case indicate that the algorithm design goals have been met, but additional empirical results and task-based assessment are needed to strengthen this conclusion.

math.OC↗

arXiv2026

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

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.

math.OC↗

arXiv2024

A directional total variation minimization algorithm for isotropic resolution in digital breast tomosynthesis

An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction. Physical DBT data is acquired with a Siemens Mammomat scanner of a structured breast phantom with ICA inserts. Results are shown for both directional TV minimization and filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the ICA objects.

physics.med-ph↗

arXiv2020

A signal detection model for quantifying over-regularization in non-linear image reconstruction

Purpose: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for non-linear image reconstruction. The vast majority of metrics employed for evaluating non-linear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to over-regularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. Methods: The metric is demonstrated in the context of a breast CT simulation, where different equal-dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a non-linear algorithm based on total variation constrained least-squares (TV-LSQ). The images are evaluated visually, with image RMSE, and with the proposed signal-detection based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Results: Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to over-regularization with TV-LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over-regularized images. Conclusions: The proposed signal detection based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when non-linear image reconstruction is used.

physics.med-ph↗