The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography. Eur Radiol. 2020;30:2525-2534. [PMID: 32006167 DOI: 10.1007/s00330-019-06571-4]

Novel motion correction algorithm improves diagnostic performance of CT fractional flow reserve

OBJECTIVES

This study aimed to investigate the diagnostic performance of computed tomography (CT) fractional flow reserve (CT-FFR) derived from standard images (STD) and images processed via first-generation (SnapShot Freeze, SSF1) and second-generation (SnapShot Freeze 2, SSF2) motion correction algorithms.

METHODS

151 patients who underwent coronary CT angiography (CCTA) and invasive coronary angiography (ICA)/FFR within 3 months were retrospectively included. CCTA images were reconstructed using an iterative reconstruction technique and then further processed through SSF1 and SSF2 algorithms. All images were divided into three groups: STD, SSF1, and SSF2. Obstructive stenosis was defined as a diameter stenosis of ≥ 50 % in the left main artery or ≥ 70 % in other epicardial vessels. Stenosis with an FFR of ≤ 0.8 or a diameter stenosis of ≥ 90 % (as revealed via ICA) was considered ischemic. In patients with multiple lesions, the lesion with lowest CT-FFR was used for patient-level analysis.

RESULTS

The overall quality score in SSF2 group (median = 3.67) was markedly higher than that in STD (median = 3) and SSF1 (median = 3) groups (P < 0.001). The best correlation (r = 0.652, P < 0.001) and consistency (mean difference = 0.04) between the CT-FFR and FFR values were observed in the SSF2 group. At the per-lesion level, CT-FFRSSF2 outperformed CT-FFRSSF1 in diagnosing ischemic lesions (area under the curve = 0.887 vs. 0.795, P < 0.001). At the per-patient level, the SSF2 group also demonstrated the highest diagnostic performance.

CONCLUSION

The SSF2 algorithm significantly improved CCTA image quality and enhanced its diagnostic performance for evaluating stenosis severity and CT-FFR calculations.

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.

Agreement of Fractional Flow Reserve Estimated by Computed Tomography With Invasively Measured Fractional Flow Reserve: A Systematic Review and Meta-Analysis

BACKGROUND

Fractional flow reserve (FFR) is the ratio of blood pressure measured distal to a stenosis and pressure proximal to a stenosis. FFR can be estimated noninvasively using computed tomography (CT) although the usefulness of this technique remains controversial. This meta-analysis evaluated the agreement of FFR estimated by CT (FFR-CT) with invasively measured FFR. The study also evaluated the diagnostic accuracy of FFR-CT, defined as the ability of FFR-CT to classify lesions as hemodynamically significant (invasive FFR ≤0.8) or insignificant (invasive FFR >0.8).

METHODS AND RESULTS

Forty-three studies reporting on 7291 blood vessels from 5236 patients were included. A moderate positive linear relationship between FFR-CT and invasively measured FFR was observed (Spearman correlation coefficient: 0.67). Agreement between the 2 measures increased as invasively measured FFR values approached 1. The overall diagnostic accuracy, sensitivity and specificity of FFR-CT were 82.2%, 80.9%, and 83.1%, respectively. Diagnostic accuracy of 90% could be demonstrated for FFR-CT values >0.90 and <0.49. The diagnostic accuracy of off-site tools was 79.4% and the diagnostic accuracy of on-site tools was 84.1%.

CONCLUSIONS

The agreement between FFR-CT and invasive FFR is moderate although agreement is highest in vessels with FFR-CT >0.9. Diagnostic accuracy varies widely with FFR-CT value but is above 90% for FFR-CT values >0.90 and <0.49. Furthermore, on-site and off-site tools have similar performance. Ultimately, FFR-CT may be a useful adjunct to CT coronary angiography as a gatekeeper for invasive coronary angiogram.

Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use

PURPOSE

Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential.

METHODS

This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed.

RESULTS

Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data.

CONCLUSIONS

Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.

Impact of coronary CT image quality on the accuracy of the FFRCT Planner

OBJECTIVE

To assess the accuracy of a virtual stenting tool based on coronary CT angiography (CCTA) and fractional flow reserve (FFR) derived from CCTA (FFRCT Planner) across different levels of image quality.

MATERIALS AND METHODS

Prospective, multicenter, single-arm study of patients with chronic coronary syndromes and lesions with FFR ≤ 0.80. All patients underwent CCTA performed with recent-generation scanners. CCTA image quality was adjudicated using the four-point Likert scale at a per-vessel level by an independent committee blinded to the FFRCT Planner. Patient- and technical-related factors that could affect the FFRCT Planner accuracy were evaluated. The FFRCT Planner was applied mirroring percutaneous coronary intervention (PCI) to determine the agreement with invasively measured post-PCI FFR.

RESULTS

Overall, 120 patients (123 vessels) were included. Invasive post-PCI FFR was 0.88 ± 0.06 and Planner FFRCT was 0.86 ± 0.06 (mean difference 0.02 FFR units, the lower limit of agreement (LLA) - 0.12, upper limit of agreement (ULA) 0.15). CCTA image quality was assessed as excellent (Likert score 4) in 48.3%, good (Likert score 3) in 45%, and sufficient (Likert score 2) in 6.7% of patients. The FFRCT Planner was accurate across different levels of image quality with a mean difference between FFRCT Planner and invasive post-PCI FFR of 0.02 ± 0.07 in Likert score 4, 0.02 ± 0.07 in Likert score 3 and 0.03 ± 0.08 in Likert score 2, p = 0.695. Nitrate dose ≥ 0.8mg was the only independent factor associated with the accuracy of the FFRCT Planner (95%CI - 0.06 to - 0.001, p = 0.040).

CONCLUSION

The FFRCT Planner was accurate in predicting post-PCI FFR independent of CCTA image quality.

CLINICAL RELEVANCE STATEMENT

Being accurate in predicting post-PCI FFR across a wide spectrum of CT image quality, the FFRCT Planner could potentially enhance and guide the invasive treatment. Adequate vasodilation during CT acquisition is relevant to improve the accuracy of the FFRCT Planner.

KEY POINTS

• The fractional flow reserve derived from coronary CT angiography (FFRCT) Planner is a novel tool able to accurately predict fractional flow reserve after percutaneous coronary intervention. • The accuracy of the FFRCT Planner was confirmed across a wide spectrum of CT image quality. Nitrates dose at CT acquisition was the only independent predictor of its accuracy. • The FFRCT Planner could potentially enhance and guide the invasive treatment.

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

OBJECTIVES

To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms.

METHODS

The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model.

RESULTS

Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone.

CONCLUSIONS

Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

The prognostic value and economic benefits of coronary angiography-derived fractional flow reserve-guided strategy in patients with coronary artery disease

Objective

This study aims to investigate the prognostic value and economic benefit of coronary angiography-derived fractional flow reserve (caFFR) guided percutaneous coronary intervention (PCI) in patients with coronary artery disease.

Methods

All patients with coronary artery disease (CAD) who underwent coronary angiography in our center between April 2021 and November 2021 were retrospectively enrolled and divided into the caFFR guidance group (n = 160) and angiography guidance group (n = 211). A threshold of caFFR≤0.8 was used for revascularization. Otherwise, delayed PCI was preferred. The patients were prospectively followed up by telephone or outpatient service at six months for major adverse cardiovascular events (MACE) of all-cause death, myocardial infarction or target vessel revascularization, stent thrombosis, and stroke. All in-hospital expenses were recorded, including initial hospitalization and re-hospitalization related to MACE.

Results

There was no significant difference in the baseline characteristics between the two groups. There were 2 (1.2%) patients in the caFFR guidance group and 5 (2.4%) patients in the angiography guidance group with MACE events during the following six months. Compared with angiography guidance, caFFR guidance reduced the revascularization rate (63.7% vs. 84.4%, p = 0.000), the average length of stents implanted (0.52 ± 0.88 vs. 1.1 ± 1.4, P < 0.001). The cost of consumables in the caFFR guidance group was significantly lower than that in the angiography guidance group (33257 ± 19595 CNY vs. 38341 ± 16485 CNY, P < 0.05).

Conclusion

Compared with coronary angiography guidance, caFFR guidance is of great significance in reducing revascularization and cost, which has significant health and economic benefits.

Optimal Measurement Sites of Coronary-Computed Tomography Angiography-derived Fractional Flow Reserve: The Insight From China CT-FFR Study

OBJECTIVES

To investigate the optimal measurement site of coronary-computed tomography angiography-derived fractional flow reserve (FFR CT ) for the assessment of coronary artery disease (CAD) in the whole clinical routine practice.

MATERIALS AND METHODS

This retrospective multicenter study included 396 CAD patients who underwent coronary-computed tomography angiography, FFR CT , and invasive FFR. FFR CT was measured at 1 cm (FFR CT -1 cm), 2 cm (FFR CT -2 cm), 3 cm (FFR CT -3 cm), and 4 cm (FFR CT -4 cm) distal to coronary stenosis, respectively. FFR CT and invasive FFR ≤0.80 were defined as lesion-specific ischemia. The diagnostic performance of FFR CT to detect ischemia was obtained using invasive FFR as the reference standard. Reduced invasive coronary angiography rate and revascularization efficiency were calculated. After a median follow-up of 35 months in 267 patients for major adverse cardiovascular events (MACE), Cox hazard proportional models were performed with FFR CT values at each measurement site.

RESULTS

For discriminating lesion-specific ischemia, the areas under the curve of FFR CT -1 cm (0.91) as well as FFR CT -2 cm (0.91) were higher than those of FFR CT -3 cm (0.89) and FFR CT -4 cm (0.88), respectively (all P <0.05). The higher reduced invasive coronary angiography rate (81.6%) was found at FFR CT -1 cm than FFR CT -2 cm (81.6% vs. 62.6%, P <0.05). Revascularization efficiency did not differ between FFR CT -1 cm and FFR CT -2 cm (80.8% vs. 65.5%, P =0.019). In 12.4% (33/267) MACE occurred and only values of FFR CT -2 cm were independently predictive of MACE (hazard ratio: 0.957 [95% CI: 0.925-0.989]; P =0.010).

CONCLUSIONS

This study indicates FFR CT -2 cm is the optimal measurement site with superior diagnostic performance and independent prognostic role.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Cardiovascular Imaging in China: Yesterday, Today, and Tomorrow

The high prevalence and mortality of cardiovascular diseases in China's large population has increased the use of cardiovascular imaging for the assessment of conditions in recent years. In this study, we review the past 20 years of cardiovascular imaging in China, the increasingly important role played by cardiovascular computed tomography in coronary artery disease and pulmonary embolism assessment, magnetic resonance imaging's use for cardiomyopathy assessment, the development and application of artificial intelligence in cardiovascular imaging, and the future of Chinese cardiovascular imaging.

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images

BACKGROUND

Fractional flow reserve derived from computed tomography (CT-FFR) can be used to noninvasively evaluate the functions of coronary arteries and has been widely welcomed in the field of cardiovascular research. However, whether different image reconstruction schemes have an effect on CT-FFR analysis through single- and multiple-cardiac periodic images in the same patient has not been investigated.

METHODS

This study retrospectively enrolled 122 patients who underwent 320-row computed tomography (CT) examination with both single- and multiple-cardiac periodic reconstruction schemes; a total of 366 coronary arteries were analyzed. The lowest CT-FFR values of each vessel and the poststenosis CT-FFR values of the lesion-specific coronary artery were measured using the two reconstruction techniques. The Wilcoxon signed-rank test was used to compare differences in CT-FFR values between the two reconstruction techniques. Spearman correlation analysis was performed to determine the relationship between CT-FFR values derived using the two methods. Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to evaluate the consistency of CT-FFR values.

RESULTS

In all blood vessels, the lowest CT-FFR values showed no significant differences between the two reconstruction techniques in the left anterior descending artery (LAD; P=0.65), left circumflex artery (LCx; P=0.46), or right coronary artery (RCA; P=0.22). In blood vessels with atherosclerotic plaques, the poststenosis CT-FFR values (2 cm distal to the maximum stenosis) exhibited no significant differences between the two reconstruction techniques in the LAD (P=0.78), LCx (P=1.00), or RCA (P=1.00). The mean CT-FFR values of single- and multiple-cardiac periodic images showed excellent correlation and minimal bias in all groups.

CONCLUSIONS

CT-FFR analysis based on an artificial intelligence deep learning neural network is stable and not affected by the type of 320-row CT reconstruction technology.

Machine learning applications in cardiac computed tomography: a composite systematic review

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

Functional CAD-RADS using FFRCT on therapeutic management and prognosis in patients with coronary artery disease

OBJECTIVES

To propose a novel functional Coronary Artery Disease-Reporting and Data System (CAD-RADS) category system integrated with coronary CT angiography (CCTA)-derived fractional flow reserve (FFR_CT) and to validate its effect on therapeutic decision and prognosis in patients with coronary artery disease (CAD).

METHODS

Firstly, we proposed a novel functional CAD-RADS and evaluated the performance of functional CAD-RADS for guiding treatment strategies with actual clinical treatment as a reference standard in a retrospective multicenter cohort with CCTA and invasive FFR performed in all patients (n = 466). Net reclassification improvement (NRI) of functional CAD-RADS over anatomical CAD-RADS was calculated. Secondly, the prognostic value of functional CAD-RADS in a prospective two-arm cohort (566 [FFR_CT arm] vs. 567 [CCTA arm]) was calculated, after a 1-year follow-up, functional CAD-RADS in FFR_CT arm (n = 513) and anatomical CAD-RADS in CCTA arm (n = 511) to determine patients at risk of adverse outcomes were compared with a Cox hazard proportional model.

RESULTS

Functional CAD-RADS demonstrated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) and comparable performance to FFR (AUC: 0.828 vs. 0.848, p = 0.253) in guiding therapeutic decisions. Functional CAD-RADS resulted in the revision of management plan as determined by anatomical CAD-RADS in 30.0% of patients (n = 140) (NRI = 0.369, p < 0.001). Functional CAD-RADS was an independent predictor for 1-year outcomes with indexes of concordance of 0.795 and the corresponding value was 0.751 in anatomical CAD-RADS.

CONCLUSION

The novel functional CAD-RADS gained incremental value in guiding therapeutic decision-making compared with anatomical CAD-RADS and comparable power in 1-year prognosis with anatomical CAD-RADS in a real-world scenario.

KEY POINTS

• The novel functional CAD-RADS category system with FFR_CT integrated into the anatomical CAD-RADS categories was originally proposed. • The novel functional CAD-RADS category system was validated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) in guiding therapeutic decisions and revised management plan in 30.0% of patients as determined by anatomical CAD-RADS (net reclassification improvement index = 0.369, p < 0.001). • Functional CAD-RADS was an independent predictor with an index of concordance of 0.795 and 0.751 in anatomical CAD-RADS for 1-year prognosis of adverse outcomes.

Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease

Background: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). Methods: In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. Results: ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. Conclusions: Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.

Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study

OBJECTIVES

To examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFR_CT) in diabetes mellitus (DM) patients.

METHODS

In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFR_CT, and invasive FFR. The patients were further grouped into mild (25~49 %), moderate (50~69 %), and severe (≥ 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score ≥ 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n  = 112) and non-DM (n  = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared.

RESULTS

Sensitivity, specificity, accuracy, and AUC of FFR_CT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFR_CT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grouping, the accuracies of FFR_CT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05).

CONCLUSIONS

DM has no negative impact on the diagnostic accuracy of machine learning-based FFR_CT.

KEY POINTS

• ML-based FFR_CT has a high discriminative accuracy of hemodynamic ischemia, which is not affected by DM. • FFR_CT was superior to the CCTA alone for the detection of ischemia relevance of coronary artery stenosis in both DM and non-DM patients. • Coronary calcification had no significant effect on the diagnostic accuracy of FFR_CT to detect ischemia in DM patients.

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

Artificial intelligence in coronary computed tomography angiography

Coronary computed tomography angiography (CCTA) is recommended as a frontline diagnostic tool in the non-invasive assessment of patients with suspected coronary artery disease (CAD) and cardiovascular risk stratification. To date, artificial intelligence (AI) techniques have brought major changes in the way that we make individualized decisions for patients with CAD. Applications of AI in CCTA have produced improvements in many aspects, including assessment of stenosis degree, determination of plaque type, identification of high-risk plaque, quantification of coronary artery calcium score, diagnosis of myocardial infarction, estimation of computed tomography-derived fractional flow reserve, left ventricular myocardium analysis, perivascular adipose tissue analysis, prognosis of CAD, and so on. The purpose of this review is to provide a comprehensive overview of current status of AI in CCTA.

Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality

OBJECTIVES

To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with invasive coronary angiography (ICA) as reference standard.

METHODS

A total of 165 patients (680 vessels and 1505 segments) were included in this study. Three sessions were performed in order: (1) The artificial intelligence (AI) software automatically processed CCTA images, stenosis degree and processing time were recorded for each case; (2) Six cardiovascular radiologists with different experiences (low/ intermediate/ high experience) independently performed image post-processing and interpretation of CCTA, (3) AI + human reading was performed. Luminal stenosis ≥50% was defined as obstructive CAD in ICA and CCTA. Diagnostic performances of AI, human reading and AI + human reading were evaluated and compared on a per-patient, per-vessel and per-segment basis with ICA as reference standard. The effects of calcification and image quality on the diagnostic performance were also studied.

RESULTS

The average post-processing and interpretation times of AI was 2.3 ± 0.6 min per case, reduced by 76%, 72%, 69% compared with low/ intermediate/ high experience readers (all P < 0.001), respectively. On a per-patient, per-vessel and per-segment basis, with ICA as reference method, the AI overall diagnostic sensitivity for detecting obstructive CAD were 90.5%, 81.4%, 72.9%, the specificity was 82.3%, 93.9%, 95.0%, with the corresponding areas under the curve (AUCs) of 0.90, 0.90, 0.87, respectively. Compared to human readers, the diagnostic performance of AI was higher than that of low experience readers (all P < 0.001). The diagnostic performance of AI + human reading was higher than human reading alone, and AI + human readers' ability to correctly reclassify obstructive CAD was also improved, especially for low experience readers (Per-patient, the net reclassification improvement (NRI) = 0.085; per-vessel, NRI = 0.070; and per-segment, NRI = 0.068, all P < 0.001). The diagnostic performance of AI was not significantly affected by calcification and image quality (all P > 0.05).

CONCLUSIONS

AI can substantially shorten the post-processing time, while AI + human reading model can significantly improve the diagnostic performance compared with human readers, especially for inexperienced readers, regardless of calcification severity and image quality.

A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management

OBJECTIVE

This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in the selection of patients for coronary intervention.

MATERIALS AND METHODS

Patients (N = 296) with symptomatic coronary artery disease identified by coronary computed tomography angiography (CTA) with stenosis over 50% were retrospectively enrolled from a single centre in this study. ICA-guided interventions were performed in patients at admission, and DL-FFRCT was conducted retrospectively. The influences on decision-making by using DL-FFRCT and the clinical outcome were compared to those of ICA-guided care for symptomatic CAD at the 2-year follow-up evaluation.

RESULT

Two hundred forty-three patients were evaluated. Up to 72% of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. A similar major adverse cardiovascular event (MACE) rate was observed in patients who underwent revascularisation with a DL-FFRCT value ≤ 0.8 (2.9%) compared to that of ICA-guided interventions (3.3%) (stented lesions with ICA stenosis > 75%) (p = 0.838).

CONCLUSION

DL-FFRCT can reduce the need for diagnostic coronary angiography when identifying patients suitable for coronary intervention. A low MACE rate was found in a 2-year follow-up investigation.

KEY POINTS

• Seventy-two percent of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. • Coronary artery stenting based on the diagnosis by using a 320-detector row CT scanner and a positive DL-FFRCT value could potentially be associated with a lower occurrence rate of major adverse cardiovascular events (2.9%) within the first 2 years. • A low event rate was found when intervention was performed in tandem lesions with haemodynamic significance based on DL-FFRCT < 0.8 as a cut-off value.

Impact of respiratory motion artifact on coronary image quality of one beat coronary CT angiography

BACKGROUND

Accuracy of CT-derived fractional flow reserve depends on good image quality. Thus, improving image quality during coronary CT angiography (CCTA) is important.

OBJECTIVE

To investigate impact of respiratory motion artifact on coronary image quality focusing on vessel diameter and territory during one beat CCTA by a 256-row detector.

METHODS

We retrospectively reviewed patients who underwent CCTA under free-breathing (n = 100) and breath-holding (n = 100), respectively. Coronary image quality is defined as 4-1 from excellent to poor (non-diagnostic) and respiratory motion artifact severity is also scored on a 4-point scale from no artifact to severe artifact. Coronary image quality and respiratory motion artifact severity of all images were evaluated by two radiologists independently.

RESULTS

Compared with free-breathing group, the image qualities are significantly higher in per-segment, per-vessel and per-patient levels (P < 0.001) and proportion of segments with excellent image quality also improves significantly (73.6% vs 60.1%, P < 0.001) in breath-holding group. The image quality improvement occurs in medium-sized coronary arterial segments. Coronary image quality improves with respiratory motion artifacts decreasing in both groups, respectively.

CONCLUSION

During one heartbeat CCTA, breath-holding is still recommended to improve coronary image quality due to improvement of the image quality in the medium-sized coronary arteries.

Influence of heart rate and coronary artery calcification on image quality and diagnostic performance of coronary CT angiography: comparison between 96-row detector dual source CT and 256-row multidetector CT

BACKGROUND

CT-derived fractional flow reserve (FFRCT) and diagnostic accuracy rely on good image quality during coronary CT angiography (CCTA).

OBJECTIVE

To investigate whether heart rate (HR) and coronary artery calcium (CAC) score decrease image quality and diagnostic performance of two advanced CT scanners including 96-row detector dual source CT (DSCT) and 256-row multidetector CT (MDCT).

METHODS

First, 79 patients who underwent CCTA (42 with DSCT and 37 with MDCT) and invasive coronary angiography (ICA) are enrolled. Next, coronary segments with excellent image quality are evaluated and the percentage is calculated. Then, diagnostic accuracy in detecting significant diameter stenosis is presented with ICA as the reference standard.

RESULTS

Compared with the DSCT, the percentage of coronary segments with excellent image quality is lower (P = 0.010) while diagnostic accuracy on per-segment level is improved (P = 0.037) using MDCT. CAC score≥400 is the only independent factor influencing the percentage of coronary segments with excellent image quality [odds ratio (OR): DSCT, 3.096 and MDCT, 1.982] and segmental diagnostic accuracy (OR: DSCT, 2.630 and MDCT, 2.336) for both scanners. HR≥70 bpm (OR: 5.506) is the independent factor influencing the percentage of coronary segments with excellent image quality with MDCT.

CONCLULSION

During CCTA, CAC score≥400 still decreases the proportion of coronary segments with excellent image quality and diagnostic accuracy with advanced CT scanners. HR≥70 bpm is another factor causing image quality decreasing with MDCT.

The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study

OBJECTIVE

To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard.

METHODS

A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard.

RESULTS

Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS.

CONCLUSION

Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification.

KEY POINTS

• CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.