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arXiv · 1706.00405

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

Sean D. Rose·Adrian A. Sanchez·Emil Y. Sidky·Xiaochuan Pan
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Abstract

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.

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Sean D. Rose, Adrian A. Sanchez, Emil Y. Sidky, Xiaochuan Pan. 2017-06-01. Investigating Simulation-Based Metrics for Characterizing Linear Iterative Reconstruction in Digital Breast Tomosynthesis. https://doi.org/10.1002/mp.12445

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