arXiv subjects

Adrian A. Sanchez

Publications and source records attributed to Adrian A. Sanchez.

3 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↗

arXiv2017

Investigating Simulation-Based Metrics for Characterizing Linear Iterative Reconstruction in Digital Breast Tomosynthesis

Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT. Three metrics based on 2D DBT simulation are investigated: (1) a root-mean-square-error (RMSE) between the test phantom and reconstructed image, (2) a gradient RMSE where the comparison is made after taking a spatial gradient of both image and phantom, and (3) a region-of-interest (ROI) Hotelling observer (HO) for signal-known-exactly/background-known-exactly (SKE/BKE) and signal-known-exactly/background-known-statistically (SKE/BKS) detection tasks. Two simulation studies are performed using the aforementioned metrics, varying voxel aspect ratio and regularization strength for two types of Tikhonov regularized least-squares optimization. The RMSE metrics are applied to a 2D test phantom and the ROI-HO metric is applied to two tasks relevant to DBT: large, low contrast lesion detection and small, high contrast microcalcification detection. The RMSE metric trends are compared with visual assessment of the reconstructed test phantom. The ROI-HO metric trends are compared with 3D reconstructed images from ACR phantom data acquired with a Hologic Selenia Dimensions DBT system. Sensitivity of image RMSE to mean pixel value is found to limit its applicability to the assessment of DBT image reconstruction. Image gradient RMSE is insensitive to mean pixel value and appears to track better with subjective visualization of the reconstructed bar-pattern phantom. The ROI-HO metric shows an increasing trend with regularization strength for both forms of Tikhonov-regularized least-squares; however, this metric saturates at intermediate regularization strength indicating a point of diminishing returns for signal detection. Visualization with reconstructed ACR phantom images appears to show a similar dependence with regularization strength.

physics.med-ph↗

arXiv2015

Task-Based Optimization of Computed Tomography Imaging Systems

The goal of this thesis is to provide a framework for the use of task-based metrics of image quality to aid in the design, implementation, and evaluation of CT image reconstruction algorithms and CT systems in general. We support the view that task-based metrics of image quality can be useful in guiding the algorithm design and implementation process in order to yield images of objectively superior quality and higher utility for a given task. Further, we believe that metrics such as the Hotelling observer (HO) SNR can be used as summary scalar metrics of image quality for the evaluation of images produced by novel reconstruction algorithms. In this work, we aim to construct a concise and versatile formalism for image reconstruction algorithm design, implementation, and assessment. The bulk of the work focuses on linear analytical algorithms, specifically the ubiquitous filtered back-projection (FBP) algorithm. However, due to the demonstrated importance of optimization-based algorithms in a wide variety of CT applications, we devote one chapter to the characterization of noise properties in TV-based iterative reconstruction, as the understanding of image statistics in optimization-based reconstruction is the limiting factor in applying HO metrics.

physics.med-ph↗