Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality. This study assessed the impact of using both higher tube currents as well as DLIR on the image quality and diagnostic accuracy. The study consisted of 51 symptomatic obese (BMI > 30 kg/m2) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent coronary computed tomography angiography (CCTA) twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube current. Images were reconstructed using ASiR-V 50% and DLIR. The image quality was evaluated by an observer using a Likert score and by ROI measurements in aorta and the myocardium. Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR resulting in an odds ratio of 1.23 (p = 0.017), and noise was reduced by 41%. A total of 88% of the image sets performed with Revolution Apex + DLIR were assessed as good enough for diagnosis compared to 69% of the image sets performed with Revolution Apex/CT + ASiR-V. In obese patients, the combination of higher tube current and DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.
Purpose: When performing coronary computed tomography angiography (CCTA) in obese patients, it is a challenge to obtain diagnostic image quality. Theoretically, higher tube currents improve image quality in obese patients by reducing noise, but this comes with an increased radiation as a trade-off. Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality without the need of a higher radiation dose. This study assessed the impact of higher tube currents and DLIR on image quality, diagnostic utility, and noise. Methods: The study consisted of 51 symptomatic obese (BMI > 30 kg/m 2 ) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent CCTA twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube currents. Images were reconstructed using ASiR-V 50% and DLIR and evaluated by an observer using a Likert score. Results: Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR, resulting in an odds ratio of 1,23 (p = 0,017), and noise was significantly reduced by 41%. Diagnostic utility was also improved with 88% vs. 69% “diagnostic” image sets of those performed with Revolution Apex + DLIR and Revolution Apex/CT + ASiR-V, respectively. No significant differences were found between Revolution CT and Revolution Apex when reconstructing with ASiR-V. Conclusion: In obese patients, the combination of higher tube currents and reconstruction with DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.
We investigated whether prediabetes diagnosed by hemoglobinA1c (HbA1c) or oral glucose tolerance test (OGTT) could predict presence and severity of coronary artery disease (CAD) in symptomatic patients. The presence of plaque, stenosis, plaque characteristics, and coronary artery calcium (CAC) were evaluated by coronary CT angiography in 702 patients with suspicion of CAD. Patients were classified by glycemic status using the American Diabetes Association criteria for HbA1c and OGTT, and compared to their respective normal ranges. Prediabetes was observed in 24% by HbA1c and 72% by OGTT. Both prediabetes classifications were associated with increased presence of plaque, stenosis, calcified plaques, CAC >400, and a lower frequency of zero CAC compared to their respective normal range (all, p < 0.05). After adjusting for potential confounders, patients with HbA1c-prediabetes had an odds ratio of 2.1 (95% CI: 1.3–3.5) for CAC >400 and 1.5 (95% CI: 1.0–2.4) for plaque presence, while none of the associations for OGTT-prediabetes were significant. The receiver operating characteristic-curve for HbA1c-prediabetes showed an area under the curve of 0.81 for CAC >400 and 0.77 for plaque presence. Prediabetes defined by HbA1c predicts presence and severity of CAD. Although OGTT identified more patients with prediabetes, their risk of CAD were not explained by prediabetes using these diagnostic-criteria.
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