Inter- and Intraoperator Variability in Measurement of On-Site CT-derived Fractional Flow Reserve Based on Structural and Fluid Analysis: A Comprehensive Analysis

Interrater variability of ML-based CT-FFR during TAVR-planning: influence of image quality and coronary artery calcifications

Objective

To compare machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) in patients before transcatheter aortic valve replacement (TAVR) by observers with differing training and to assess influencing factors.

Background

Coronary computed tomography angiography (cCTA) can effectively exclude CAD, e.g. prior to TAVR, but remains limited by its specificity. CT-FFR may mitigate this limitation also in patients prior to TAVR. While a high reliability of CT-FFR is presumed, little is known about the reproducibility of ML-based CT-FFR.

Methods

Consecutive patients with obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers. Categorization into hemodynamically significant CAD was compared against invasive coronary angiography. The influence of image quality and coronary artery calcium score (CAC) was examined.

Results

CT-FFR was successfully performed on 214/272 examinations by both observers. The median difference of CT-FFR between both observers was -0.05(-0.12-0.02) (p < 0.001). Differences showed an inverse correlation to the absolute CT-FFR values. Categorization into CAD was different in 37/214 examinations, resulting in net recategorization of Δ13 (13/214) examinations and a difference in accuracy of Δ6.1%. On patient level, correlation of absolute and categorized values was substantial (0.567 and 0.570, p < 0.001). Categorization into CAD showed no correlation to image quality or CAC (p > 0.13).

Conclusion

Differences between CT-FFR values increased in values below the cut-off, having little clinical impact. Categorization into CAD differed in several patients, but ultimately only had a moderate influence on diagnostic accuracy. This was independent of image quality or CAC.

Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve

BACKGROUND

Advances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFR_CT).

PURPOSE

To assess the interoperator and intraoperator variability of coronary CT angiography-derived FFR_CT using a machine learning-based postprocessing prototype.

MATERIALS AND METHODS

We included 60 symptomatic patients who underwent coronary CT angiography. FFR_CT was calculated by two independent operators after training using a machine learning-based on-site prototype. FFR_CT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate interoperator variability effect in FFR_CT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.

RESULTS

A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFR_CT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 to 0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 to 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared with proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis.

CONCLUSION

A high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFR_CT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFR_CT.

Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia

BACKGROUND

Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFR_ML) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFR_ML compared to stress perfusion cardiovascular magnetic resonance (CMR) and tested if there is an additional value of CT-FFR_ML over coronary computed tomography angiography (cCTA).

METHODS

Our retrospective analysis included 269 vessels in 141 patients (mean age 67 ± 9 years, 78% males) who underwent clinically indicated cCTA and subsequent stress perfusion CMR within a period of 2 months. CT-FFR_ML values were calculated from standard cCTA.

RESULTS

CT-FFR_ML revealed no hemodynamic significance in 79% of the patients having ≥ 50% stenosis in cCTA. Chi² values for the statistical relationship between CT-FFR_ML and stress perfusion CMR was significant (p < 0.0001). CT-FFR_ML and cCTA (≥ 70% stenosis) provided a per patient sensitivity of 88% (95%CI 64-99%) and 59% (95%CI 33-82%); specificity of 90% (95%CI 84-95%) and 85% (95%CI 78-91%); positive predictive value of 56% (95%CI 42-69%) and 36% (95%CI 24-50%); negative predictive value of 98% (95%CI 94-100%) and 94% (95%CI 90-96%); accuracy of 90% (95%CI 84-94%) and 82% (95%CI 75-88%) when compared to stress perfusion CMR. The accuracy of cCTA (≥ 50% stenosis) was 19% (95%CI 13-27%). The AUCs were 0.89 for CT-FFR_ML and 0.74 for cCTA (≥ 70% stenosis) and therefore significantly different (p < 0.05).

CONCLUSION

CT-FFR_ML compared to stress perfusion CMR as the reference standard shows high diagnostic power in the identification of patients with hemodynamically significant coronary artery stenosis. This could support the role of cCTA as gatekeeper for further downstream testing and may reduce the number of patients undergoing unnecessary invasive workup.

Prognosis of ischemia recurrence in patients with moderate intracranial atherosclerotic disease using quantitative MRA measurements

PURPOSE

To investigate the relation between delayed ischemic stroke and the intracranial atherosclerotic disease (ICAD) hemodynamics as determined by Non-invasive Optimal Vessel Analysis (NOVA) MRI measurements.

MATERIALS AND METHODS

Thirty-three patients with ICAD were enrolled in this study. All patients underwent clinically indicated angioplasty followed by 2-dimensional phase contrast MR (2D PCMR) performed on a 3.0 Tesla MRI scanner using either a 16-channel neurovascular coil or 32-channel head coil. The volumetric flow rate measurements were calculated from 2D PCMR with Non-invasive Optimal Vessel Analysis (NOVA) software (VasSol, Chicago, IL, USA). Flow rate measurements were obtained in 20 major arteries distal, proximal and within the Circle of Willis. Patients were followed up for six month, and ischemia reoccurrence and location were recorded. Receiver operating characteristic (ROC) analysis was performed using flow rates measurements in the ipsilateral side of the ischemic event occurrence.

RESULTS

Complete set of measurements was achieved in n=34. Left and right hemisphere ischemia recurrence was observed in seven and three cases respectively. Best predictor of ischemic event reoccurrence was flow rate in the middle cerebral artery with area under the ROC of 0.821±0.109.

CONCLUSIONS

This is an effectiveness study to determine whether blood flow measurements in the intracranial vasculature may be predictive of future ischemic events. Our results demonstrated significant correlation between the blood flow measurements using 2D PCMR processed with the NOVA software and the reoccurrence of ischemia. These results support further investigation for using this method for risk stratification of ICAD patients.

Incremental Diagnostic Value of CT Fractional Flow Reserve Using Subtraction Method in Patients with Severe Calcification: A Pilot Study

Although on-site workstation-based CT fractional flow reserve (CT-FFR) is an emerging method for assessing vessel-specific ischemia in coronary artery disease, severe calcification is a significant factor affecting CT-FFR’s diagnostic performance. The subtraction method significantly improves the diagnostic value with respect to anatomic stenosis for patients with severe calcification in coronary CT angiography (CCTA). We evaluated the diagnostic capability of CT-FFR using the subtraction method (subtraction CT-FFR) in patients with severe calcification. This study included 32 patients with 45 lesions with severe calcification (Agatston score >400) who underwent both CCTA and subtraction CCTA using 320-row area detector CT and also received invasive FFR within 90 days. The diagnostic capabilities of CT-FFR and subtraction CT-FFR were compared. The sensitivities, specificities, positive predictive values (PPVs), and negative predictive values (NPVs) of CT-FFR vs. subtraction CT-FFR for detecting hemodynamically significant stenosis, defined as FFR ≤ 0.8, were 84.6% vs. 92.3%, 59.4% vs. 75.0%, 45.8% vs. 60.0%, and 90.5% vs. 96.0%, respectively. The area under the curve for subtraction CT-FFR was significantly higher than for CT-FFR (0.84 vs. 0.70) (p = 0.04). The inter-observer and intra-observer variabilities of subtraction CT-FFR were 0.76 and 0.75, respectively. In patients with severe calcification, subtraction CT-FFR had an incremental diagnostic value over CT-FFR, increasing the specificity and PPV while maintaining the sensitivity and NPV with high reproducibility.

Patient-specific 3D-printed coronary models based on coronary computed tomography angiography volumes to investigate flow conditions in coronary artery disease

BACKGROUND

3D printed patient-specific coronary models have the ability to enable repeatable benchtop experiments under controlled blood flow conditions. This approach can be applied to CT-derived patient geometries to emulate coronary flow and related parameters such as Fractional Flow Reserve (FFR).

METHODS

This study uses 3D printing to compare such benchtop FFR results with a non-invasive CT-FFR research software algorithm and catheter based invasive FFR (I-FFR) measurements. Fifty-two patients with a clinical indication for I-FFR underwent a research Coronary CT Angiography (CCTA) prior to catheterization. CT images were used to measure CT-FFR and to generate patient-specific 3D printed models of the aortic root and three main coronary arteries. Each patient-specific model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for two coronary outflow rates ('normal', 250 ml min^-1; and 'hyperemic', 500 ml min^-1) by adjusting the model's distal coronary resistance.

RESULTS

Pearson correlations and ROC AUC were calculated using invasive I-FFR as reference. The Pearson correlation factor of CT-FFR and B-FFR-500 was 0.75 and 0.71, respectively. Areas under the ROCs for CT-FFR and B-FFR-500 were 0.80 (95%CI: 0.70-0.87) and 0.81 (95%CI: 0.64-0.91) respectively.

CONCLUSION

Benchtop flow simulations with 3D printed models provide the capability to measure pressure changes at any location in the model, for ultimately emulating the FFR at several simulated physiological blood flow conditions.

CLINICAL TRIAL REGISTRATION

https://clinicaltrials.gov/show/NCT03149042.

Multimodal Multiparametric Three-dimensional Image Fusion in Coronary Artery Disease: Combining the Best of Two Worlds

PURPOSE

To allow for comprehensive noninvasive diagnostics of coronary artery disease (CAD) by using three-dimensional (3D) image fusion of CT coronary angiography, CT-derived fractional flow reserve (CT FFR), whole-heart dynamic 3D cardiac MRI perfusion, and 3D cardiac MRI late gadolinium enhancement (LGE).

MATERIALS AND METHODS

Seventeen patients (54 years ± 10 [standard deviation], one female) who underwent cardiac CT and cardiac MRI were included (combined subcohort of three prospective trials). Software facilitating multimodal 3D image fusion was developed. Postprocessing of CT data included segmentation of the coronary tree and heart contours, calculation of CT FFR values, and color coding of the coronary tree according to CT FFR. Postprocessing of cardiac MRI data included segmentation of the left ventricle (LV) in cardiac MRI perfusion and cardiac MRI LGE, co-registration of cardiac MRI to CT data, and projection of cardiac MRI perfusion and LGE values onto the high spatial resolution LV from CT.

RESULTS

Image quality was rated as good to excellent (scores: 2.5-2.6; 3 = excellent). CT coronary angiography revealed significant stenoses in seven of 17 cases (41%). CT FFR was possible in 16 of 17 cases (94%) and showed pathologic flow in seven of 17 cases (41%), six of which coincided with cases revealing significant stenoses at CT coronary angiography. Cardiac MRI perfusion identified eight of 17 patients (47%) with hypoperfusion (ischemic burden of 17% ± 5). Cardiac MRI LGE showed myocardial scar in three of 17 cases (18%, scar burden of 7% ± 4). Conventional two-dimensional readout of CT coronary angiography and cardiac MRI resulted in eight of 17 cases (47%) with uncertain findings. Most of these divergent findings could be solved when adding information from CT FFR and 3D image fusion (six of eight, 75%).

CONCLUSION

Multimodal 3D cardiac image fusion is feasible and may help with comprehensive noninvasive CAD diagnostics.Supplemental material is available for this article.© RSNA, 2020.