CT in Transcatheter-delivered Treatment of Valvular Heart Disease.

Minimally invasive strategies to treat valvular heart disease have emerged over the past 2 decades. The use of transcatheter aortic valve replacement in the treatment of severe aortic stenosis, for example, has recently expanded from high- to low-risk patients and became an alternative treatment for those with prohibitive surgical risk. With the increase in transcatheter strategies, multimodality imaging, including echocardiography, CT, fluoroscopy, and cardiac MRI, are used. Strategies for preprocedural imaging strategies vary depending on the targeted valve. Herein, an overview of preprocedural imaging strategies and their postprocessing approaches is provided, with a focus on CT. Transcatheter aortic valve replacement is reviewed, as well as less established minimally invasive treatments of the mitral and tricuspid valves. In addition, device-specific details and the goals of CT imaging are discussed. Future imaging developments, such as peri-procedural fusion imaging, machine learning for image processing, and mixed reality applications, are presented.

Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes.

Background The role of CT angiography-derived fractional flow reserve (CT-FFR) in pre-transcatheter aortic valve replacement (TAVR) assessment is uncertain. Purpose To evaluate the predictive value of on-site machine learning-based CT-FFR for adverse clinical outcomes in candidates for TAVR. Materials and Methods This observational retrospective study included patients with severe aortic stenosis referred to TAVR after coronary CT angiography (CCTA) between September 2014 and December 2019. Clinical end points comprised major adverse cardiac events (MACE) (nonfatal myocardial infarction, unstable angina, cardiac death, or heart failure admission) and all-cause mortality. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm. The ability of CT-FFR (abnormal if ≤0.75) to predict outcomes and improve the predictive value of the current noninvasive work-up was assessed. Survival analysis was performed, and the C-index was used to assess the performance of each predictive model. To compare nested models, the likelihood ratio χ2 test was performed. Results A total of 196 patients (mean age ± standard deviation, 75 years ± 11; 110 women [56%]) were included; the median time of follow-up was 18 months. MACE occurred in 16% (31 of 196 patients) and all-cause mortality in 19% (38 of 196 patients). Univariable analysis revealed CT-FFR was predictive of MACE (hazard ratio [HR], 4.1; 95% CI: 1.6, 10.8; P = .01) but not all-cause mortality (HR, 1.2; 95% CI: 0.6, 2.2; P = .63). CT-FFR was independently associated with MACE (HR, 4.0; 95% CI: 1.5, 10.5; P = .01) when adjusting for potential confounders. Adding CT-FFR as a predictor to models that include CCTA and clinical data improved their predictive value for MACE (P = .002) but not all-cause mortality (P = .67), and it showed good discriminative ability for MACE (C-index, 0.71). Conclusion CT angiography-derived fractional flow reserve was associated with major adverse cardiac events in candidates for transcatheter aortic valve replacement and improved the predictive value of coronary CT angiography assessment. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Choe in this issue.

Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

OBJECTIVES: To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA). METHODS: Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA. RESULTS: Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). CONCLUSION: Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. KEY POINTS: • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study.

OBJECTIVES: To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis. METHODS: In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up. RESULTS: In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04). CONCLUSION: In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy. KEY POINTS: • In stable patients with intermediate stenosis, ML-based FFRCT strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy. • Compared with the CCTA strategy, ML-based FFRCTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFRCT strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.

Additive value of epicardial adipose tissue quantification to coronary CT angiography-derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia.

OBJECTIVES: Epicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia. METHODS: Patients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard. RESULTS: Fifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05). CONCLUSIONS: EAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis. KEY POINTS: • CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia. • EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia. • A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

Utility of Functional and Volumetric Left Atrial Parameters Derived From Preprocedural Cardiac CTA in Predicting Mortality After Transcatheter Aortic Valve Replacement.

BACKGROUND. Cardiac CTA is required for preprocedural workup before transcatheter aortic valve replacement (TAVR) and can be used to assess functional parameters of the left atrium (LA). OBJECTIVE. We aimed to evaluate the utility of functional and volumetric LA parameters derived from cardiac CTA to predict mortality in patients with severe aortic stenosis (AS) undergoing TAVR. METHODS. This retrospective study included 175 patients with severe AS (92 men, 83 women; median age, 79.0 years) who underwent cardiac CTA for clinical pre-TAVR assessment. A postdoctoral research fellow calculated maximum and minimum LA volumes using biplane area-length measurements; these values were indexed to body surface area, and maximum and minimum LA volume index (LAVImax and LAVImin, respectively) values were calculated. The LA emptying fraction (LAEF) was automatically calculated. All-cause mortality within a 24-month follow-up period after TAVR was recorded. To identify parameters predictive of mortality, Cox regression analysis was performed, and results were summarized by hazard ratio (HR) and 95% CI. The Harrell C-index was used to assess model performance. A radiology resident repeated the measurements in a random sample of 20% (n = 35) of the cases, and interobserver agreement was computed using the intraclass correlation coefficient (ICC). RESULTS. Thirty-eight deaths (21.7%) were recorded within a median follow-up of 21 months. LAVImax (HR, 1.02 [95% CI, 1.01-1.04]; p = .01), LAVImin (HR, 1.02 [95% CI, 1.01-1.04]; p < .001), and LAEF (HR, 0.97 [95% CI, 0.95-0.99]; p = .002) were predictive of mortality in univariable analysis. After adjusting for clinical parameters, only LAEF (HR, 0.97 [95% CI, 0.94-0.99]; p = .02) independently predicted mortality. The C-index of the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) significantly increased from 0.636 to 0.683, 0.694, and 0.700 when incorporating into the model LAVImax, LAVImin, and LAEF, respectively. The ICC for maximum and minimum LA volumes and LAEF ranged from 0.94 to 0.99. CONCLUSION. LAEF derived from preprocedural cardiac CTA independently predicts mortality in patients with severe AS undergoing TAVR. CLINICAL IMPACT. Cardiac CTA-derived LA function, evaluated during pre-TAVR workup, can be used to assess preprocedural risk and may improve risk stratification in post-TAVR surveillance.

Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation.

OBJECTIVES: To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFRCT) in patients who have undergone stents implantation. METHODS: Firstly, the feasibility of FFRCT in stented vessels was validated. The diagnostic performance of FFRCT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFRCT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFRCT measurements (FFRCT, ΔFFRCT, ΔFFRCT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFRCT. The primary endpoint was major adverse cardiovascular events (MACE). RESULTS: Per-patient accuracy of FFRCT was 0.85 in identifying hemodynamically ISR. FFRCT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFRCT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032-1.177]; p = 0.004) and follow-up ΔFFRCT/length (HR, 1.014 [95% CI, 1.006-1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594-0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846. CONCLUSIONS: Noninvasive ML-based FFRCT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients. KEY POINTS: • Machine-learning-based FFRCT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFRCT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCT in patients with moderate-to-high or high risk needs to be further studied. • FFRCT might refine the clinical pathway of patients with stent implantation to invasive catheterization.

A fully automated software platform for structural mitral valve analysis.

OBJECTIVE: To evaluate a novel fully automated mitral valve analysis software platform for cardiac computer tomography angiography (CCTA)-based structural heart therapy procedure planning. METHODS: The study included 52 patients (25 women; mean age, 66.9 ± 12.4 years) who had undergone CCTA prior to transcatheter mitral valve replacement (TMVR) or surgical mitral valve intervention (replacement or repair). Therapeutically relevant mitral valve annulus parameters (projected area, circumference, trigone-to-trigone (T-T) distance, anterior-posterior (AP) diameter, and anterolateral-posteromedial (AL-PM) diameter) were measured. Results of the fully automated mitral valve analysis software platform with and without manual adjustments were compared with the reference standard of a user-driven measurement program (3mensio, Pie Medical Imaging). Measurements were compared between the fully automated software, both with and without manual adjustment, and the user-driven program using intraclass correlation coefficients (ICC). A secondary analysis included the time to obtain all measurements. RESULTS: Fully automated measurements showed a good to excellent agreement (circumference, ICC = 0.70; projected area, ICC = 0.81; T-T distance, ICC = 0.64; AP, ICC = 0.62; and AL-PM diameter, ICC = 0.78) compared with the user-driven analysis. There was an excellent agreement between fully automated measurement with manual adjustments and user-driven analysis regarding circumference (ICC = 0.91), projected area (ICC = 0.93), T-T distance (ICC = 0.80), AP (ICC = 0.78), and AL-PM diameter (ICC = 0.79). The time required for mitral valve analysis was significantly lower using the fully automated software with manual adjustments compared with the standard assessment (134.4 ± 36.4 s vs. 304.3 ± 77.7 s) (p < 0.01). CONCLUSION: The fully automated mitral valve analysis software, when combined with manual adjustments, demonstrated a strong correlation compared with the user-driven software while reducing the total time required for measurement. KEY POINTS: • The novel software platform allows for a fully automated analysis of mitral valve structures. • An excellent agreement was found between the fully automated measurement with manual adjustments and the user-driven analysis. • The software showed quicker measurement time compared with the standard analysis of the mitral valve.

Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease.

OBJECTIVES: This study investigated the impact of machine learning (ML)-based fractional flow reserve derived from computed tomography (FFRCT) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD). METHODS: One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFRCT values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFRCT and severe stenosis on qualitative CCTA and ICA were also evaluated. RESULTS: After FFRCT results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4-48 months), FFRCT ≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11); p < 0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73), p = 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14, p = 0.045). Reserving ICA and revascularization for vessels with positive FFRCT could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions. CONCLUSIONS: This study indicated ML-based FFRCT had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFRCT may direct therapeutic decision-making with the potential to improve efficiency of ICA. KEY POINTS: • ML-based FFRCT shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA. • FFRCT noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD. • Reserving ICA and revascularization for vessels with positive FFRCT may reduce the normalcy rate of ICA and improve its efficiency.

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

OBJECTIVES: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard. METHODS: Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard. RESULTS: One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93). CONCLUSION: Coronary CTA-derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power. KEY POINTS: • Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling.

Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFRML)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFRCFD and FFRML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFRML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFRCFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFRML and 0.89 for FFRCFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFRML (AUC, 0.91) and FFRCFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFRML was significantly shorter compared with that of FFRCFD (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.

Diagnostic accuracy of low and high tube voltage coronary CT angiography using an X-ray tube potential-tailored contrast medium injection protocol.

OBJECTIVES: To compare the diagnostic accuracy between low-kilovolt peak (kVp) (≤ 100) and high-kVp (> 100) third-generation dual-source coronary CT angiography (CCTA) using a kVp-tailored contrast media injection protocol. METHODS: One hundred twenty patients (mean age = 62.6 years, BMI = 29.0 kg/m2) who underwent catheter angiography and CCTA with automated kVp selection were separated into two cohorts (each n = 60, mean kVp = 84 and 117). Contrast media dose was tailored to the kVp level: 70 = 40 ml, 80 = 50 ml, 90 = 60 ml, 100 = 70 ml, 110 = 80 ml, and 120 = 90 ml. Contrast-to-noise ratio (CNR) was measured. Two observers evaluated image quality and the presence of significant coronary stenosis (> 50% luminal narrowing). RESULTS: Diagnostic accuracy (sensitivity/specificity) with ≤ 100 vs. > 100 kVp CCTA was comparable: per patient = 93.9/92.6% vs. 90.9/92.6%, per vessel = 91.5/97.8% vs. 94.0/96.8%, and per segment = 90.0/96.7% vs. 90.7/95.2% (all P > 0.64). CNR was similar (P > 0.18) in the low-kVp vs. high-kVp group (12.0 vs. 11.1), as ws subjective image quality (P = 0.38). Contrast media requirements were reduced by 38.1% in the low- vs. high-kVp cohort (53.6 vs. 86.6 ml, P < 0.001) and radiation dose by 59.6% (4.3 vs. 10.6 mSv, P < 0.001). CONCLUSIONS: Automated tube voltage selection with a tailored contrast media injection protocol allows CCTA to be performed at ≤ 100 kVp with substantial dose reductions and equivalent diagnostic accuracy for coronary stenosis detection compared to acquisitions at > 100 kVp. KEY POINTS: • Low-kVp coronary CT angiography (CCTA) enables reduced contrast and radiation dose. • Diagnostic accuracy is comparable between ≤ 100 and > 100 kVp CCTA. • Image quality is similar for low- and high-kVp CCTA. • Low-kVp image acquisition is facilitated by automated tube voltage selection. • Tailoring contrast injection protocols to the automatically selected kVp-level is feasible.

Coronary CT Angiography-derived Fractional Flow Reserve.

Invasive coronary angiography (ICA) with measurement of fractional flow reserve (FFR) by means of a pressure wire technique is the established reference standard for the functional assessment of coronary artery disease (CAD) ( 1 , 2 ). Coronary computed tomographic (CT) angiography has emerged as a noninvasive method for direct assessment of CAD and plaque characterization with high diagnostic accuracy compared with ICA ( 3 , 4 ). However, the solely anatomic assessment provided with both coronary CT angiography and ICA has poor discriminatory power for ischemia-inducing lesions. FFR derived from standard coronary CT angiography (FFRCT) data sets by using any of several advanced computational analytic approaches enables combined anatomic and hemodynamic assessment of a coronary lesion by a single noninvasive test. Current technical approaches to the calculation of FFRCT include algorithms based on full- and reduced-order computational fluid dynamic modeling, as well as artificial intelligence deep machine learning ( 5 , 6 ). A growing body of evidence has validated the diagnostic accuracy of FFRCT techniques compared with invasive FFR. Improved therapeutic guidance has been demonstrated, showing the potential of FFRCT to streamline and rationalize the care of patients suspected of having CAD and improve outcomes while reducing overall health care costs ( 7 , 8 ). The purpose of this review is to describe the scientific principles, clinical validation, and implementation of various FFRCT approaches, their precursors, and related imaging tests. © RSNA, 2017.

Low contrast medium-volume third-generation dual-source computed tomography angiography for transcatheter aortic valve replacement planning.

PURPOSE: To investigate feasibility, image quality and safety of low-tube-voltage, low-contrast-volume comprehensive cardiac and aortoiliac CT angiography (CTA) for planning transcatheter aortic valve replacement (TAVR). MATERIALS AND METHODS: Forty consecutive TAVR candidates prospectively underwent combined CTA of the aortic root and vascular access route (270 mgI/ml iodixanol). Patients were assigned to group A (second-generation dual-source CT [DSCT], 100 kV, 60 ml contrast, 4.0 ml/s flow rate) or group B (third-generation DSCT, 70 kV, 40 ml contrast, 2.5 ml/s flow rate). Vascular attenuation, noise, signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were compared. Subjective image quality was assessed by two observers. Estimated glomerular filtration (eGFR) at CTA and follow-up were measured. RESULTS: Besides a higher body-mass-index in group B (24.8±3.8 kg/m2 vs. 28.1±5.4 kg/m2, P=0.0339), patient characteristics between groups were similar (P≥0.0922). Aortoiliac SNR (P=0.0003) was higher in group B. Cardiac SNR (P=0.0003) and CNR (P=0.0181) were higher in group A. Subjective image quality was similar (P≥0.213) except for aortoiliac image noise (4.42 vs. 4.12, P=0.0374). TAVR-planning measurements were successfully obtained in all patients. There were no significant changes in eGFR among and between groups during follow-up (P≥0.302). CONCLUSION: TAVR candidates can be safely and effectively evaluated by a comprehensive CTA protocol with low contrast volume using low-tube-voltage acquisition. KEY POINTS: • Third-generation dual-source CT facilitates low-tube-voltage acquisition. • TAVR planning can be performed with reduced contrast volume and radiation dose. • TAVR-planning CT did not result in changes in creatinine levels at follow-up. • TAVR candidates can be safely evaluated by comprehensive low-tube-voltage CT angiography.

Diagnostic accuracy of coronary CT angiography using 3rd-generation dual-source CT and automated tube voltage selection: Clinical application in a non-obese and obese patient population.

PURPOSE: To investigate diagnostic accuracy of 3rd-generation dual-source CT (DSCT) coronary angiography in obese and non-obese patients. METHODS: We retrospectively analyzed 76 patients who underwent coronary CT angiography (CCTA) and invasive coronary angiography. Prospectively ECG-triggered acquisition was performed with automated tube voltage selection (ATVS). Patients were dichotomized based on body mass index in groups A (<30 kg/m2, n = 37) and B (≥30 kg/m2, n = 39) and based on tube voltage in groups C (<120 kV, n = 46) and D (120 kV, n = 30). Coronary arteries were assessed for significant stenoses (≥50 % luminal narrowing) and diagnostic accuracy was calculated. RESULTS: Per-patient overall sensitivity, specificity, positive predictive value, negative predictive value (NPV) and accuracy were 96.9 %, 95.5 %, 93.9 %, 97.7 % and 96.1 %, respectively. Sensitivity and NPV were lower in groups B and D compared to groups A and C, but no statistically significant differences were observed (group A vs. B: sensitivity, 100.0 % vs. 93.3 %, p = 0.9493; NPV, 100 % vs. 95.5 %, p = 0.9812; group C vs. D: sensitivity, 100.0 % vs. 92.3 %, p = 0.8462; NPV, 100.0 % vs. 94.1 %, p = 0.8285). CONCLUSION: CCTA using 3rd-generation DSCT and (ATVS) provides high diagnostic accuracy in both non-obese and obese patients. KEY POINTS: • Coronary CTA provides high diagnostic accuracy in non-obese and obese patients. • Diagnostic accuracy between obese and non-obese patients showed no significant difference. • <120 kV studies were performed in 44 % of obese patients. • Current radiation dose-saving approaches can be applied independent of body habitus.

Correlation and predictive value of aortic root calcification markers with coronary artery calcification and obstructive coronary artery disease.

OBJECTIVE: To evaluate the correlation between aortic root calcification (ARC) markers and coronary artery calcification (CAC) derived from coronary artery calcium scoring (CACS) and their ability to predict obstructive coronary artery disease (CAD). METHODS: We retrospectively analyzed 189 patients (47% male, age 60.3 ± 11.1 years) with an intermediate probability of CAD who underwent clinically indicated CACS and coronary CT angiography (CCTA). ARC markers [aortic root calcium score (ARCS) and volume (ARCV)] were calculated and compared to CAC markers: coronary artery calcium score (CACS), volume (CACV), and mass (CACM). CCTA datasets were visually evaluated for significant CAD (stenosis ≥ 50%) and the ability of ARC markers to predict obstructive CAD was assessed. RESULTS: ARCS (mean 67.7 ± 189.5) and ARCV (mean 67.3 ± 184.7) showed significant differences between patients with and without CAC (109.4 ± 238.6 vs 9.42 ± 31.4, p < 0.0001; 108.5 ± 232.4 vs 9.9 ± 30.5, p < 0.0001). A strong correlation was found for ARCS and ARCV with CACS, CACM, and CACV (all p < 0.0001). In a multivariate analysis, ARCS (OR 1.09, p = 0.033) and ARCV (OR 1.12, p = 0.046) were independent markers for CAC. Using a receiver-operating characteristics analysis, the AUC to detect severe CAC was 0.71 (p < 0.0001) and 0.71 (p < 0.0001) for ARCS and ARCV, respectively. ARCS (0.67, p < 0.0001) and ARCV (0.68, p < 0.0001) showed discriminatory power for predicting obstructive CAD, yielding sensitivities 61 and 78% and specificities of 62 and 80%, respectively. CONCLUSION: ARC markers are associated with and independently predict the presence of CAC and obstructive CAD. Further testing is required in patients with severe ARC and significant CAD in order to reliably obtain these markers from thoracic-CT or X-ray for proper risk classification.

Image quality, radiation dose, and diagnostic accuracy of prospectively ECG-triggered high-pitch coronary CT angiography at 70 kVp in a clinical setting: comparison with invasive coronary angiography.

PURPOSE: To investigate image quality, radiation dose, and diagnostic performance of prospectively ECG-triggered high-pitch coronary CT angiography (CCTA) at 70 kVp compared to invasive coronary angiography (ICA) as reference standard. MATERIALS AND METHODS: Forty-three patients underwent prospectively ECG-triggered high-pitch CCTA at 70 kVp using 30 cc (11 g iodine) contrast medium and ICA. Subjective and objective image quality was evaluated for each CCTA study. CCTA performance for diagnosing ≥50% stenosis was assessed. Results were stratified according to heart rate (HR), body mass index (BMI), Agatston score, and image quality. RESULTS: At CCTA, 94.3% (500/530) of coronary segments were of diagnostic quality. Using ICA as reference standard, sensitivity and accuracy were 100% and 93.0% on a per-patient basis. Per-vessel and per-segment performances were 92.2% and 89.5%; 79.5% and 88.3%, respectively. No differences were found in diagnostic accuracy between different HR, BMI, and calcification subgroups (all P > 0.05) on a per-patient basis. However, low image quality reduced diagnostic accuracy on a per-patient, per-vessel and per-segment basis (all P < 0.05). The mean effective radiation dose was 0.2 ± 0.0 mSv. CONCLUSION: Our presented protocol results in an effective radiation dose of 0.2 mSv and high diagnostic accuracy for stenosis detection in a selected, non-obese population. KEY POINTS: Prospectively ECG-triggered high-pitch CCTA at 70 kVp is feasible. This protocol has a high diagnostic accuracy for stenosis detection. The mean effective radiation dose was 0.2 ± 0.0 mSv. Only 30 cc of contrast material is used in this protocol. Low image quality reduced diagnostic accuracy of CCTA.

Dual-source CT imaging to plan transcatheter aortic valve replacement: accuracy for diagnosis of obstructive coronary artery disease.

PURPOSE: To assess the accuracy of computed tomographic (CT) examinations performed for the purpose of transcatheter aortic valve replacement (TAVR) planning to diagnose obstructive coronary artery disease (CAD). MATERIALS AND METHODS: With institutional review board approval, waivers of informed consent, and in compliance with HIPAA, 100 consecutive TAVR candidates (61 men, mean age 79.6 years ± 9.9) who underwent both TAVR planning CT (with a dual-source CT system) and coronary catheter (CC) angiographic imaging were retrospectively analyzed. At both modalities, the presence of stenosis in the native coronary arteries was assessed. Additionally, all coronary bypass grafts were rated as patent or occluded. With CC angiographic imaging as the reference standard, the accuracy of CT for lesion detection on a per-vessel and per-patient basis was calculated. The accuracy of CT for the assessment of graft patency was also analyzed. RESULTS: For per-vessel and per-patient analysis for the detection of stenosis that was 50% or more in the native coronary arteries, CT imaging had, respectively, 94.4% and 98.6% sensitivity, 68.4% and 55.6% specificity, 94.7% and 93.8% negative predictive value (NPV), and 67.0% and 85.7% positive predictive value. Per-patient sensitivity of stenosis 50% or greater with CT for greater than 70% stenosis at CC angiographic imaging was 100%. All 12 vessels in which percutaneous coronary intervention was performed were correctly identified as demonstrating stenosis 50% or greater with CT. There was agreement between CT and CC angiographic imaging regarding graft patency in 114 of 115 grafts identified with CC angiographic imaging. CONCLUSION: TAVR planning CT has high sensitivity and NPV in excluding obstructive CAD. An additional preprocedural CC angiographic examination may not be required in TAVR candidates with a CT examination that does not show obstructive CAD.

Coronary CT angiography-derived fractional flow reserve correlated with invasive fractional flow reserve measurements--initial experience with a novel physician-driven algorithm.

OBJECTIVES: The present study aimed to determine the feasibility of a novel fractional flow reserve (FFR) algorithm based on coronary CT angiography (cCTA) that permits point-of-care assessment, without data transfer to core laboratories, for the evaluation of potentially ischemia-causing stenoses. METHODS: To obtain CT-based FFR, anatomical coronary information and ventricular mass extracted from cCTA datasets were integrated with haemodynamic parameters. CT-based FFR was assessed for 36 coronary artery stenoses in 28 patients in a blinded fashion and compared to catheter-based FFR. Haemodynamically relevant stenoses were defined by an invasive FFR ≤0.80. Time was measured for the processing of each cCTA dataset and CT-based FFR computation. Assessment of cCTA image quality was performed using a 5-point scale. RESULTS: Mean total time for CT-based FFR determination was 51.9 ± 9.0 min. Per-vessel analysis for the identification of lesion-specific myocardial ischemia demonstrated good correlation (Pearson's product-moment r = 0.74, p < 0.0001) between the prototype CT-based FFR algorithm and invasive FFR. Subjective image quality analysis resulted in a median score of 4 (interquartile ranges, 3-4). CONCLUSIONS: Our initial data suggest that the CT-based FFR method for the detection of haemodynamically significant stenoses evaluated in the selected population correlates well with invasive FFR and renders time-efficient point-of-care assessment possible. KEY POINTS: • CT-based FFR computation is a promising novel non-invasive application. • A novel prototype algorithm permits time-efficient point-of-care CT-based FFR assessment. • Initial results of the CT-based FFR prototype algorithm compare favourably with FFR.

CT myocardial perfusion imaging.

OBJECTIVE. CT myocardial perfusion imaging is rapidly becoming an important adjunct to coronary CT angiography for the anatomic and functional assessment of coronary artery disease with a single modality. Existing techniques for CT myocardial perfusion imaging include static techniques, which provide a snapshot of the myocardial blood pool, and dynamic techniques. CONCLUSION. This review provides a systematic overview of the presently available approaches for the assessment of myocardial perfusion at CT, including diagnostic accuracy and limitations.