The Western Denmark Cardiac Computed Tomography Registry:a review and validation study

BACKGROUND: As a subregistry to the Western Denmark Heart Registry (WDHR), the Western Denmark Cardiac Computed Tomography Registry (WDHR-CCTR) is a clinical database established in 2008 to monitor and improve the quality of cardiac computed tomography (CT) in Western Denmark. OBJECTIVE: We examined the content, data quality, and research potential of the WDHR-CCTR. METHODS: We retrieved 2008–2012 data to examine the 1) content; 2) completeness of procedure registration using the Danish National Patient Registry as reference; 3) completeness of variable registration comparing observed vs expected numbers; and 4) positive predictive values as well as negative predictive values of 19 main patient and procedure variables. RESULTS: By December 31, 2012, almost 22,000 cardiac CTs with up to 40 variables for each procedure have been registered. Of these, 87% were coronary CT angiography performed in patients with symptoms indicative of coronary artery disease. Compared with the Danish National Patient Registry, the overall procedure completeness was 72%. However, an additional medical record review of 282 patients registered in the Danish National Patient Registry, but not in the WDHR-CCTR, showed that coronary CT angiographies accounted for only 23% of all nonregistered cardiac CTs, indicating >90% completeness of coronary CT angiographies in the WDHR-CCTR. The completeness of individual variables varied substantially (range: 0%–100%), but was >85% for more than 70% of all variables. Using medical record review of 250 randomly selected patients as reference standard, the positive predictive value for the 19 variables ranged from 89% to 100% (overall 97%), whereas the negative predictive value ranged from 97% to 100% (overall 99%). Stratification by center status showed consistently high positive and negative predictive values for both university (96%/99%) and nonuniversity centers (97%/99%). CONCLUSION: WDHR-CCTR provides ongoing prospective registration of all cardiac CTs performed in Western Denmark since 2008. Overall, the registry data have a high degree of completeness and validity, making it a valuable tool for clinical epidemiological research

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography

Background Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. Purpose To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Material and Methods Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. Results VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR (P = 0.004). The objective measures showed significant differences between FBP and 60% ASIR (P < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. Conclusion ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR

Is There Any Improvement in Image Quality in Obese Patients When Using a New X-ray Tube and Deep Learning Image Reconstruction in Coronary Computed Tomography Angiography?

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