We report on an optimization-based image reconstruction algorithm for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm is designed to enable quantitative imaging of Iodine-based contrast agent by mitigating the depth blur artifact. The depth blurring is controlled by exploiting gradient sparsity of the contrast agent distribution. We find that minimization of directional total variation (TV) is particularly effective at exploiting gradient sparsity for the DBT scan configuration. In this initial work, the contrast agent imaging is performed by reconstructing images from DBT data acquired at source potentials of 30- and 49-kV, followed by weighted subtraction to suppress background glandular structure and isolate the contrast agent distribution. The algorithm is applied to DBT data, acquired with a Siemens Mammomat scanner, of a structured breast phantom with Iodine contrast agent inserts. Results for both in-plane and transverse-plane imaging for directional TV minimization are presented alongside images reconstructed by filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the Iodine-based contrast agent objects.
In this work, we focus on developing a channelized Hotelling observer (CHO) that estimates ideal linear observer performance on signal detection in images resulting from non-linear image reconstruction in computed tomography. In particular, many options on specifying the channel functions are explored. A hybrid channel model is proposed where a set of traditional Laguerre-Gauss functions are concatenated with a set of central pixel functions. This expanded channel set allows the CHO to perform robustly over a wide range of image reconstruction and system parameters. The application of this model observer to determining of the total-variation constrained least-squares algorithm yields images that are seen to favor detection of small, subtle signals.
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