Novel fast FFR derived from coronary CT angiography based on static first-pass algorithm:a comparison study

BACKGROUND Fractional flow reserve(FFR)is the invasive gold standard for evaluating coronary arterial stenosis.However,there have been a few non-invasive methods such as computational fluid dynamics FFR(CFD-FFR)with coronary CT angiography(CCTA)images that can perform FFR assessment.This study aims to develop a new method based on the principle of static first-pass of CT perfusion imaging technique(SF-FFR)and evaluate the efficacy in direct comparisons between CFD-FFR and the invasive FFR.METHODS A total of 91 patients(105 coronary artery vessels)who were admitted from January 2015 to March 2019 were enrolled in this study,retrospectively.All patients underwent CCTA and invasive FFR.64 patients(75 coronary artery vessels)were successfully analyzed.The correlation and diagnostic performance of SF-FFR method on per-vessel basis were analyzed,using invasive FFR as the gold standard.As a comparison,we also evaluated the correlation and diagnostic performance of CFD-FFR.RESULTS The SF-FFR showed a good Pearson correlation(r=0.70,P<0.001)and intra-class correlation(r=0.67,P<0.001)with the gold standard.The Bland-Altman analysis showed that the average difference between the SF-FFR and invasive FFR was 0.03(0.11–0.16);between CFD-FFR and invasive FFR was 0.04(-0.10–0.19).Diagnostic accuracy and area under the ROC curve on a per-vessel level were 0.89,0.94 for SF-FFR,and 0.87,0.89 for CFD-FFR,respectively.The SF-FFR calculation time was about 2.5 s per case while CFD calculation was about 2 min on an Nvidia Tesla V100 graphic card.CONCLUSIONS The SF-FFR method is feasible and shows high correlation compared to the gold standard.This method could simplify the calculation procedure and save time compared to the CFD method.

Performance evaluation of deep learning-based post-processing and diagnostic reporting system for coronary CT angiography: a clinical comparative study

To the Editor:Coronary computed tomography angiography(CCTA)has been increasingly,widely performed for diagnosing coronary artery,disease,lAnatomical diagnosis,that is,stenosis grading,is stillthe main diagnostic index provided'by most CCTA tests.Post-processing and interpretation of stenosis are 2 essential'steps that need to be performed bycardiovascular imaging professionals from scan completion to diagnosis conclusion,which is repetitive and time-consuming,taking an average of 30 minutes each case in China and becoming the bottleneck and gradually creating an imbalance between supply and demand.In ine with the rapid development of artificial intelligence(Al)technology in recent years,it has been expected to solve these specific problems.We developed an AI system for automating post-processing and diagnostic reporting of CCTA data using deep learning algorithms to establishanew1-clickworkflowforeverydayuse,namely,CCTA-AI(Figure 1).To further assess its capabilities,this study intends to answer 2 following questions:To what extent can it improve the efficiency of post-processing?To what extent can CCTA-AI detect and calculate coronary artery stenosis due to each atherosclerotic plaque?