Investigating simulation‐based metrics for characterizing linear iterative reconstruction in digital breast tomosynthesis

Abstract

Purpose Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT. Methods Three metrics based on a 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 with resolution bar patterns at varying angles, and the ROI-HO metric is applied to two tasks relevant to DBT: lesion detection, modeled by use of a large, low-contrast signal, and microcalcification detection, modeled by use of a small, high-contrast signal. The RMSE metric trends are compared with visual assessment of the reconstructed bar-pattern phantom. The ROI-HO metric trends are compared with 3D reconstructed images from ACR phantom data acquired with a Hologic Selenia Dimensions DBT system. Results Sensitivity of the image RMSE to mean pixel value is found to limit its applicability to the assessment of DBT image reconstruction. The 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 the reconstructed ACR phantom images appear to show a similar dependence with regularization strength. Conclusions From the limited studies presented it appears that image gradient RMSE trends correspond with visual assessment better than image RMSE for DBT image reconstruction. The ROI-HO metric for both detection tasks also appears to reflect visual trends in the ACR phantom reconstructions as a function of regularization strength. We point out, however, that the true utility of these metrics can only be assessed after amassing more data.

A Novel Task-Based reconstruction approach for digital breast tomosynthesis

“…where φ : R m → R denotes a regularization function that aims at improving the robustness to noise Sechopoulos (2013b); Rose et al (2019Rose et al ( , 2017; Dang et al (2017). Herein, φ can take several possible forms to enforce spatial regularity, range constraints, or sparsity in a possibly transformed domain Pustelnik et al (2016).…”

“…As DBT volume reconstruction is an ill-posed inverse problem, iterative reconstruction algorithms have demonstrated their superiority over analytical ones Sechopoulos (2013b). In particular, regularized iterative algorithms have the potential to incorporate prior knowledge aiming at mitigating the missing information issue Sidky et al (2007Sidky et al ( , 2009; Metin et al (2014); Piccolomini and Morotti (2016); Szasz et al (2016); Rose et al (2017); Dang et al (2017). Even though these regularized approaches yield smoother DBT reconstructed volumes, they do not account for the aforementioned clinical task, i.e.…”

Imaging of fiber-like structures in digital breast tomosynthesis

“…26. We also consider two common forms of penalized, least-squares optimization that we have already investigated in the context of signal detection in DBT 21,22 .…”

DIR‐DBTnet: Deep iterative reconstruction network for three‐dimensional digital breast tomosynthesis imaging

“…For example, the filtered backprojection (FBP) algorithm with modified ramp filter could be used to improve the mass detectability. 5,6 Additionally, the iterative reconstruction (IR) algorithms could be used to deal with the ill-posed inverse problem [7][8][9] in DBT imaging. For the IR algorithm, a certain objective function containing the data fidelity term and the regularization term needs to be well-designed and optimized.…”