Interleaved x-ray imaging: A method for simultaneous acquisition of quantitative and diagnostic digital subtraction angiography.

BACKGROUND: Flow altering angiographic procedures suffer from ill-defined, qualitative endpoints. Quantitative digital subtraction angiography (qDSA) is an emerging technology that aims to address this issue by providing intra-procedural blood velocity measurements from time-resolved, 2D angiograms. To date, qDSA has used 30 frame/s DSA imaging, which is associated with high radiation dose rate compared to clinical diagnostic DSA (up to 4 frame/s). PURPOSE: The purpose of this study is to demonstrate an interleaved x-ray imaging method which decreases the radiation dose rate associated with high frame rate qDSA while simultaneously providing low frame rate diagnostic DSA images, enabling the acquisition of both datasets in a single image sequence with a single injection of contrast agent. METHODS: Interleaved x-ray imaging combines low radiation dose image frames acquired at a high rate with high radiation dose image frames acquired at a low rate. The feasibility of this approach was evaluated on an x-ray system equipped with research prototype software for x-ray tube control. qDSA blood velocity quantification was evaluated in a flow phantom study for two lower dose interleaving protocols (LD1: 3.7 ± 0.02 mGy / s $3.7 \pm 0.02\ {\mathrm{mGy}}/{\mathrm{s}}$ and LD2: 1.7 ± 0.04 mGy / s $1.7 \pm 0.04{\mathrm{\ mGy}}/{\mathrm{s}}$ ) and one conventional (full dose) protocol ( 11.4 ± 0.04 mGy / s ) $11.4 \pm 0.04{\mathrm{\ mGy}}/{\mathrm{s}})$ . Dose was measured at the interventional reference point. Fluid velocities ranging from 24 to 45 cm/s were investigated. Gold standard velocities were measured using an ultrasound flow probe. Linear regression and Bland-Altman analysis were used to compare ultrasound and qDSA. RESULTS: The LD1 and LD2 interleaved protocols resulted in dose rate reductions of -67.7% and -85.5%, compared to the full dose qDSA scan. For the full dose protocol, the Bland-Altman limits of agreement (LOA) between qDSA and ultrasound velocities were [0.7, 6.7] cm/s with a mean difference of 3.7 cm/s. The LD1 interleaved protocol results were similar (LOA: [0.3, 6.9] cm/s, bias: 3.6 cm/s). The LD2 interleaved protocol resulted in slightly larger LOA: [-2.5, 5.5] cm/s with a decrease in the bias: 1.5 cm/s. Linear regression analysis showed a strong correlation between ultrasound and qDSA derived velocities using the LD1 protocol, with a R 2 ${R}^2$ of 0.96 $0.96$ , a slope of 1.05 $1.05$ and an offset of 1.9 $1.9$  cm/s. Similar values were also found for the LD2 protocol, with a R 2 ${R}^2$ of 0.93 $0.93$ , a slope of 0.98 $0.98$ and an offset of 2.0 $2.0$  cm/s. CONCLUSIONS: The interleaved method enables simultaneous acquisition of low-dose high-rate images for intra-procedural blood velocity quantification (qDSA) and high-dose low-rate images for vessel morphology evaluation (diagnostic DSA).

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

OBJECTIVE: Advancements 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. METHODS: Monochromatic, 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. RESULTS: Slice 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. CONCLUSIONS: Deep 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.

Prototype system for interventional dual-energy subtraction angiography.

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 contrast-enhanced acquisition.