RESUMO
Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.
RESUMO
KW-3902 (a newly synthesized adenosine A1-receptor antagonist) has potent diuretic and renal protective activities. We investigated the influence of the emulsion formulation on the pharmacokinetics of KW-3902 and its metabolite (M1) in rats using three different formulations, i.e., a lipid emulsion about 130 nm in diameter composed of egg yolk lecithin: soybean oil: oleic acid=1:1:0.048, a liposome about 100 nm in diameter composed of egg yolk lecithin, and a saline solution containing 1% (v/v) each of dimethyl sulfoxide and 1 N NaOH. There was no significant difference in the pharmacokinetic parameters of KW-3902 (elimination half-life (T1/2), area under the plasma concentration-time curves (AUC0-infinity), total body clearance (CL), mean residence time (MRT) and volume of distribution at steady-state (Vdss) and M1 (Cmax, T1/2, AUC0-infinity and MRT) after injection of these three dosage forms. Moreover, we investigated in vitro the binding of KW-3902 to blood components using these three formulations. KW-3902 was completely partitioned into the blood components regardless of its dosage form. These findings suggested that KW-3902 dissociated rapidly from the lipid emulsion or liposome in blood after injection and showed intrinsic pharmacokinetics of KW-3902 at doses of 0.1 and 1 mg/kg. Thus, the lipid emulsion formulation of KW-3902 was defined as a solvent, which was a vehicle for dissolving the drugs to prepare the injection, at its expected effective doses.
