Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
| Autor: | Mohamed Marwan, Silvia Smolka, Stephan Achenbach, M. Moshage, Daniel O. Bittner, F. Ammon, Markus Goeller |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
medicine.medical_specialty
Computed Tomography Angiography Coronary Artery Disease Iterative reconstruction Fractional flow reserve Coronary Angiography Severity of Illness Index Coronary artery disease Predictive Value of Tests medicine Humans Radiology Nuclear Medicine and imaging ddc:610 Retrospective Studies Computed tomography angiography medicine.diagnostic_test business.industry Coronary Stenosis Curve analysis General Medicine Gold standard (test) medicine.disease Coronary Vessels Fractional Flow Reserve Myocardial Stenosis Kernel (statistics) Radiology Tomography X-Ray Computed business |
| Popis: | Objectives We evaluated the influence of image reconstruction kernels on the diagnostic accuracy of CT-derived fractional flow reserve (FFRCT) compared to invasive FFR in patients with coronary artery disease. Methods Sixty-nine patients, in whom coronary CT angiography was performed and who were further referred for invasive coronary angiography with FFR measurement via pressure wire, were retrospectively included. CT data sets were acquired using a third-generation dual-source CT system and rendered with medium smooth (Bv40) and sharp (Bv49) reconstruction kernels. FFRCT was calculated on-site using prototype software. Coronary stenoses with invasive FFR ≤ 0.80 were classified as significant. Agreement between FFRCT and invasive FFR was determined for both reconstruction kernels. Results One hundred analyzed vessels in 69 patients were included. Twenty-five vessels were significantly stenosed according to invasive FFR. Using a sharp reconstruction kernel for FFRCT resulted in a significantly higher correlation with invasive FFR (r = 0.74, p r = 0.58, p p = 0.04) and a higher AUC in ROC curve analysis to correctly identify/exclude significant stenosis (AUC = 0.92 vs. AUC = 0.82 for sharp vs. medium smooth kernel, respectively, p = 0.02). A FFRCT value of ≤ 0.8 using a sharp reconstruction kernel showed a sensitivity of 88% and a specificity of 92% for detecting ischemia-causing lesions, resulting in a diagnostic accuracy of 91%. The medium smooth reconstruction kernel performed worse (sensitivity 60%, specificity 89%, accuracy 82%). Conclusion Compared to invasively measured FFR, FFRCT using a sharp image reconstruction kernel shows higher diagnostic accuracy for detecting lesions causing ischemia, potentially altering decision-making in a clinical setting. Key Points • Image reconstruction parameters influence the diagnostic accuracy of simulated fractional flow reserve derived from coronary computed tomography angiography. • Using a sharp kernel image reconstruction algorithm delivers higher diagnostic accuracy compared to medium smooth kernel image reconstruction (gold standard invasive fractional flow reserve). |
| Databáze: | OpenAIRE |
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