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Dual-energy subtraction angiography (DESA) using fast kV switching has received attention for its potential to reduce misregistration artifacts in thoracic and abdominal imaging where patient motion is difficult to control; however, commercial interventional solutions are not currently available. The purpose of this work was to adapt an x-ray angiography system for 2D and 3D DESA. The platform for the dual-energy prototype was a commercially available x-ray angiography system with a flat panel detector and an 80 kW x-ray tube. Fast kV switching was implemented using custom x-ray tube control software that follows a user-defined switching program during a rotational acquisition. Measurements made with a high temporal resolution kV meter were used to calibrate the relationship between the requested and achieved kV and pulse width. To enable practical 2D and 3D imaging experiments, an automatic exposure control algorithm was developed to estimate patient thickness and select a dual-energy switching technique (kV and ms switching) that delivers a user-specified task CNR at the minimum air kerma to the interventional reference point. An XCAT-based simulation study conducted to evaluate low and high energy image registration for the scenario of 30-60 frame/s pulmonary angiography with respiratory motion found normalized RMSE values ranging from 0.16% to 1.06% in tissue-subtracted DESA images, depending on respiratory phase and frame rate. Initial imaging in a porcine model with a 60 kV, 10 ms, 325 mA / 120 kV, 3.2 ms, 325 mA switching technique demonstrated an ability to form tissue-subtracted images from a single contrastenhanced acquisition.

ObjectiveAdvancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT.MethodsMonochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations.ResultsSlice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images.ConclusionsDeep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, “plastic”) relative to FBP.

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