Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard.

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

Rationale and Design of the CREDENCE Trial: computed TomogRaphic evaluation of atherosclerotic DEtermiNants of myocardial IsChEmia.

BACKGROUND: Coronary computed tomography angiography (CCTA) allows for non-invasive assessment of obstructive coronary artery disease (CAD) beyond measures of stenosis severity alone. This assessment includes atherosclerotic plaque characteristics (APCs) and calculation of fractional flow reserve (FFR) from CCTA (FFRCT). Similarly, stress imaging by myocardial perfusion scintigraphy (MPS) provides vital information. To date, the diagnostic performance of integrated CCTA assessment versus integrated MPS assessment for diagnosis of vessel-specific ischemia remains underexplored. METHODS: CREDENCE will enroll adult individuals with symptoms suspicious of CAD referred for non-emergent invasive coronary angiography (ICA), but without known CAD. All participants will undergo CCTA, MPS, ICA and FFR. FFR will be performed for lesions identified at the time of ICA to be ≥40 and <90 % stenosis, or those clinically indicated for evaluation. Study analyses will focus on diagnostic performance of CCTA versus MPS against invasive FFR reference standard. An integrated stenosis-APC-FFRCT metric by CCTA for vessel-specific ischemia will be developed from derivation cohort and tested against a validation cohort. Similarly, integrated metric by MPS for vessel-specific ischemia will be developed, validated and compared. An FFR value of ≤0.80 will be considered as ischemia causing. The primary endpoint will be the diagnostic accuracy of vessel territory-specific ischemia of integrated stenosis-APC-FFRCT measure by CCTA, compared with perfusion or perfusion-myocardial blood flow stress imaging testing, against invasive FFR. DISCUSSION: CREDENCE will determine the performance of integrated CCTA metric compared to integrated MPS measure for diagnosis of vessel-specific ischemia. If proven successful, this study may reduce the number of missed diagnoses and help to optimally predict ischemia-causing lesions. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02173275 . Registered on June 23, 2014.

Automated Quantitative Plaque Burden from Coronary CT Angiography Noninvasively Predicts Hemodynamic Significance by using Fractional Flow Reserve in Intermediate Coronary Lesions.

PURPOSE: To evaluate the utility of multiple automated plaque measurements from coronary computed tomographic (CT) angiography in determining hemodynamic significance by using invasive fractional flow reserve (FFR) in patients with intermediate coronary stenosis. MATERIALS AND METHODS: The study was approved by the institutional review board. All patients provided written informed consent. Fifty-six intermediate lesions (with 30%-69% diameter stenosis) in 56 consecutive patients (mean age, 62 years; range, 46-88 years), who subsequently underwent invasive coronary angiography with assessment of FFR (values ≤0.80 were considered hemodynamically significant) were analyzed at coronary CT angiography. Coronary CT angiography images were quantitatively analyzed with automated software to obtain the following measurements: volume and burden (plaque volume × 100 per vessel volume) of total, calcified, and noncalcified plaque; low-attenuation (<30 HU) noncalcified plaque; diameter stenosis; remodeling index; contrast attenuation difference (maximum percent difference in attenuation per unit area with respect to the proximal reference cross section); and lesion length. Logistic regression adjusted for potential confounding factors, receiver operating characteristics, and integrated discrimination improvement were used for statistical analysis. RESULTS: FFR was 0.80 or less in 21 (38%) of the 56 lesions. Compared with nonischemic lesions, ischemic lesions had greater diameter stenosis (65% vs 52%, P = .02) and total (49% vs 37%, P = .0003), noncalcified (44% vs 33%, P = .0004), and low-attenuation noncalcified (9% vs 4%, P < .0001) plaque burden. Calcified plaque and remodeling index were not significantly different. In multivariable analysis, only total, noncalcified, and low-attenuation noncalcified plaque burden were significant predictors of ischemia (P < .015). For predicting ischemia, the area under the receiver operating characteristics curve was 0.83 for total plaque burden versus 0.68 for stenosis (P = .04). CONCLUSION: Compared with stenosis grading, automatic quantification of total, noncalcified, and low-attenuation noncalcified plaque burden substantially improves determination of lesion-specific hemodynamic significance by FFR in patients with intermediate coronary lesions.

Role of computed tomography for diagnosis and risk stratification of patients with suspected or known coronary artery disease.

Cardiac computed tomographic angiography (CCTA) has emerged as a powerful imaging modality for the detection and prognostication of individuals with suspected coronary artery disease. Because calcification of coronary plaque occurs in proportion to the total atheroma volume, the initial diagnostic potential of CCTA focused on the identification and quantification of coronary calcium in low- to intermediate-risk individuals, a finding that tracks precisely with the risk of incident adverse clinical events. Beyond noncontrast detection of coronary calcium, CCTA using iodinated contrast yields incremental information about the degree and distribution of coronary plaques and stenosis, as well as vessel wall morphology and atherosclerotic plaque features. This additive information offers the promise of CCTA to provide a more comprehensive view of total atherosclerotic burden because it relates to myocardial ischemia and future adverse clinical events. Furthermore, emerging data suggest the prognostic and diagnostic importance of stenosis severity detection and atherosclerotic plaque features described by CCTA including positive remodeling, low-attenuation plaque, and spotty calcification, which have been associated with the vulnerability of plaque. We report a summary of the evidence supporting the role of CCTA in the detection of subclinical and clinical coronary artery disease in both asymptomatic and symptomatic patients and discuss the potential of CCTA to augment the identification of at-risk individuals. CCTA and coronary artery calcium scoring offer the ability to improve risk stratification, discrimination, and reclassification of the risk in patients with suspected coronary artery disease and to noninvasively determine the measures of stenosis severity and atherosclerotic plaque features.

Quantitative high-efficiency cadmium-zinc-telluride SPECT with dedicated parallel-hole collimation system in obese patients: results of a multi-center study.

BACKGROUND: Obesity is a common source of artifact on conventional SPECT myocardial perfusion imaging (MPI). We evaluated image quality and diagnostic performance of high-efficiency (HE) cadmium-zinc-telluride parallel-hole SPECT MPI for coronary artery disease (CAD) in obese patients. METHODS AND RESULTS: 118 consecutive obese patients at three centers (BMI 43.6 ± 8.9 kg·m(-2), range 35-79.7 kg·m(-2)) had upright/supine HE-SPECT and invasive coronary angiography > 6 months (n = 67) or low likelihood of CAD (n = 51). Stress quantitative total perfusion deficit (TPD) for upright (U-TPD), supine (S-TPD), and combined acquisitions (C-TPD) was assessed. Image quality (IQ; 5 = excellent; < 3 nondiagnostic) was compared among BMI 35-39.9 (n = 58), 40-44.9 (n = 24) and ≥45 (n = 36) groups. ROC curve area for CAD detection (≥50% stenosis) for U-TPD, S-TPD, and C-TPD were 0.80, 0.80, and 0.87, respectively. Sensitivity/specificity was 82%/57% for U-TPD, 74%/71% for S-TPD, and 80%/82% for C-TPD. C-TPD had highest specificity (P = .02). C-TPD normalcy rate was higher than U-TPD (88% vs 75%, P = .02). Mean IQ was similar among BMI 35-39.9, 40-44.9 and ≥45 groups [4.6 vs 4.4 vs 4.5, respectively (P = .6)]. No patient had a nondiagnostic stress scan. CONCLUSIONS: In obese patients, HE-SPECT MPI with dedicated parallel-hole collimation demonstrated high image quality, normalcy rate, and diagnostic accuracy for CAD by quantitative analysis of combined upright/supine acquisitions.

CFR and FFR assessment with PET and CTA: strengths and limitations.

Positron emission tomography (PET) myocardial perfusion imaging (MPI) has high diagnostic accuracy and prognostic value. PET-MPI can also be used to quantitatively evaluate regional myocardial blood flow (MBF). This technique also allows the calculation of the coronary flow reserve (CFR)/myocardial flow reserve (MFR), which is the ratio of MBF at peak hyperemia to resting MBF. Coronary computed tomography angiography (CTA) is a non-invasive method for accurate detection and exclusion of high-grade coronary stenoses, when compared to an invasive coronary angiography reference standard. However, CTA assessment of coronary stenoses tends toward overestimation, and CTA cannot determine physiologic significance of lesions. Recent advances in computational fluid dynamics and image-based modeling permit calculation of non-invasive fractional flow reserve derived from CT (FFRCT), without the need for additional imaging, modification of acquisition protocols, or administration of medications. In this review, we cover the CFR/MFR assessment by PET and FFR assessment by CT.

Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).

Quantification and characterisation of coronary artery plaque volume and adverse plaque features by coronary computed tomographic angiography: a direct comparison to intravascular ultrasound.

OBJECTIVE: We evaluated the performance of manual measures of coronary plaque volumes and atherosclerotic plaque features from coronary CT angiography (CTA), using intravascular ultrasound (IVUS) as the reference. METHODS: Thirty individual coronary plaques with suitable fiduciary markers were identified. Plaque volumes on coronary CTA were manually quantified by two observers and compared to IVUS plaque volumes as interpreted by an independent laboratory. The presence of adverse plaque characteristics-low attenuation plaque (LAP), positive remodelling (PR) and spotty calcification (SC)-on coronary CTA was evaluated and compared to IVUS. RESULTS: High correlation in plaque volumes was detected between observers (r = 0.94, P < 0.0001; 95 % limits of agreement <48.7 mm(3), bias 6.6 mm(3)). Excellent correlation (r = 0.95, P < 0.0001) was noted in plaque volume between independent observers and IVUS (95 % limits of agreement <40.6 mm(3), bias -4.4 mm(3)) and did not differ from IVUS (105.0 ± 56.7 vs. 109.4 ± 60.7 mm(3), P = 0.2). The frequency of LAP (10 % vs. 17 %), PR (7 % vs. 10 %) and SC (27 % vs. 33 %) was similar between coronary CTA and IVUS (all P = NS). CONCLUSIONS: Plaque volume on coronary CTA determined by manual methods demonstrates high correlation and modest agreement to IVUS. Further, coronary CTA demonstrates high accuracy for the identification of adverse plaque characteristics, including LAP, PR and SC. KEY POINTS: • Coronary CT angiography is a non-invasive test that enables coronary plaque assessment • Plaque quantification by coronary CT angiography correlates well with intravascular ultrasound findings • Coronary CT angiography can identify adverse plaque characteristics.

AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.

BACKGROUND: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations. OBJECTIVES: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity.

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.

Diabetes, Atherosclerosis, and Stenosis by AI.

OBJECTIVE: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown. RESEARCH DESIGN AND METHODS: We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed. RESULTS: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions. CONCLUSIONS: Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP.

Automatic alignment of myocardial perfusion PET and 64-slice coronary CT angiography on hybrid PET/CT.

BACKGROUND: Hybrid PET/CT allows for acquisition of cardiac PET and coronary CT angiography (CCTA) in one session. However, PET and CCTA are acquired with differing breathing protocols and require software registration. We aimed to validate automatic correction for breathing misalignment between PET and CCTA acquired on hybrid scanner. METHODS: Single-session hybrid PET/CT studies of rest/stress (13)N-ammonia PET and CCTA in 32 consecutive patients were considered. Automated registration of PET left ventricular (LV) surfaces with CCTA volumes was evaluated by comparing with expert manual alignment by two observers. RESULTS: The average initial misalignments between the position of LV on PET and CCTA were 27.2 ± 11.8, 13.3 ± 11.5, and 14.3 ± 9.1 mm in x, y, and z axes on rest, and 26.3 ± 10.2, 11.1 ± 9.5, and 11.7 ± 7.1 mm in x, y, and z axes on stress, respectively. The automated PET-CCTA co-registration had 95% agreement as judged visually. Compared with expert manual alignment, the translation errors of the algorithm were 5.3 ± 2.8 mm (rest) and 6.0 ± 3.5 mm (stress). 3D visualization of combined coronary vessel anatomy and hypoperfusion from PET could be made without further manual adjustments. CONCLUSION: Software co-registration of CCTA and PET myocardial perfusion imaging on hybrid PET/CT scanners is necessary, but can be performed automatically, facilitating integrated 3D display on PET/CT.

Automated quantitative Rb-82 3D PET/CT myocardial perfusion imaging: normal limits and correlation with invasive coronary angiography.

BACKGROUND: We aimed to characterize normal limits and to determine the diagnostic accuracy for an automated quantification of 3D 82-Rubidium (Rb-82) PET/CT myocardial perfusion imaging (MPI). METHODS: We studied 125 consecutive patients undergoing Rb-82 PET/CT MPI, including patients with suspected coronary artery disease (CAD) and invasive coronary angiography, and 42 patients with a low likelihood (LLk) of CAD. Normal limits for perfusion and function were derived from LLk patients. QPET software was used to quantify perfusion abnormality at rest and stress expressed as total perfusion deficit (TPD). RESULTS: Relative perfusion databases did not differ in any of the 17 segments between males and females. The areas under the receiver operating characteristic curve for detection of CAD were 0.86 for identification of ≥50% and ≥70% stenosis. The sensitivity/specificity was 86%/86% for detecting ≥50% stenosis and 93%/77% for ≥70% stenosis, respectively. In regard to normal limits, mean rest and stress left ventricular ejection fraction (LVEF) were 67% ± 10% and 75% ± 9%, respectively. Mean transient ischemic dilation ratio was 1.06 ± 0.14 and mean increase in LVEF with stress was 7.4% ± 6.1% (95th percentile of 0%). CONCLUSION: Normal limits have been established for 3D Rb-82 PET/CT analysis with QPET software. Fully automated quantification of myocardial perfusion PET data shows high diagnostic accuracy for detecting obstructive CAD.

Prognostic value of quantitative high-speed myocardial perfusion imaging.

BACKGROUND: We studied the prognostic value of fully automated quantitative analysis software applied to new solid-state, high-speed (HS) SPECT-myocardial perfusion imaging (MPI). METHODS: 1,613 consecutive patients undergoing exercise or adenosine HS-MPI were followed for 2.6 ± 0.5 years for all-cause mortality (ACM). Automated quantitative software was used to compute stress total perfusion deficit (sTPD) and was compared to semi-quantitative visual analysis. MPI was characterized as 0% (normal), 1%-4% (minimal perfusion defect), 5%-10% (mildly abnormal), and >10% (moderately/severely abnormal). RESULTS: During follow-up, 79 patients died (4.9%). Annualized ACM increased with progressively increasing sTPD; 0% (0.87%), 1%-4% (1.94%), 5%-10% (3.10%), and >10% (5.33%) (log-rank P < .0001). While similar overall findings were observed with visual analysis, only sTPD demonstrated increased risk in patients with minimal perfusion defects. In multivariable analysis, sTPD > 10% was a mortality predictor (HR 3.03, 95% CI 1.30-7.09, P = .01). Adjusted mortality rate was substantial in adenosine MPI, but low in exercise MPI (9.0% vs 1.0%, P < .0001). CONCLUSIONS: By quantitative analysis, ACM increases with increasing perfusion abnormality among patients undergoing stress HS-MPI. These findings confirm previous results obtained with visual analysis using conventional Anger camera imaging systems.

Coronary artery CT low-density plaque area and its ratio to the whole area of a non-calcified plaque at the culprit lesion in patients with unstable and stable coronary artery disease.

Intravascular ultrasound studies have shown that patients with unstable angina pectoris (UAP) more frequently had soft plaques in the culprit coronary arteries than patients with stable angina pectoris (SAP). We evaluated coronary plaque characteristics of culprit lesions in patients with UAP by 64-slice computed tomographic coronary angiography (64-slice CTCA). 64-slice CTCA (Aquilion 64, Toshiba Medical Systems, Otawara, Japan) was performed in 30 patients (UAP = 14, SAP = 16) before percutaneous coronary intervention (PCI). Coronary plaque area was measured by manual tracing for the difference between the area within the external elastic membrane and the area of the vessel lumen at the site of maximal luminal narrowing as observed on a cross-sectional 64-slice CTCA image where PCI was performed. Within this plaque area, CT low-density plaque area (< 50 Hounsfield units) was automatically calculated. There were no differences in stenotic rate and whole plaque area of the culprit lesion between patients with UAP and SAP. However, the CT low-density plaque area was significantly greater in patients with UAP than in those with SAP. A greater area of CT low-density plaque in the culprit lesion is associated with UAP rather than SAP. Measuring CT-low density plaque area on 64-slice CTCA images could be useful for understanding the clinical setting of UAP.

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography.

OBJECTIVES: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. BACKGROUND: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. METHODS: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). RESULTS: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. CONCLUSION: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. CONDENSED ABSTRACT: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.

Automatic 3D registration of dynamic stress and rest (82)Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction.

PURPOSE: The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and (82)Rb were demonstrated. METHODS: The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n = 16) and (82)Rb (n = 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 ((82)Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers. RESULTS: The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for (82)Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 ± 1.1, 1.4 ± 0.7, 1.9 ± 1.3) mm and (2.0 ± 1.1, 1.2 ±0 .9, 1.9 ± 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 ± 0.7, 0.7 ± 0.6, 1.3 ± 0.6) mm and (2.0 ± 0.9, 0.6 ± 0.4, 1.2 ± 1.2) mm for (82)Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be ≥2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress (82)Rb studies and 4.3% in rest (82)Rb studies. CONCLUSIONS: Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.

Prognostic value of automated vs visual analysis for adenosine stress myocardial perfusion SPECT in patients without prior coronary artery disease: a case-control study.

PURPOSE: We aimed to evaluate the prognostic value of automated quantitative hypoperfusion parameters derived from adenosine stress myocardial perfusion SPECT (MPS) for predicting sudden or cardiac death (CD) in case-controlled patients with suspected coronary artery disease (CAD). METHODS: We considered patients with available adenosine stress Tc-99m sestamibi MPS scans and follow-up information. 81 CD patients from a registry of 428 patients documented by the National Death Index were directly matched in a retrospective case-control design to patients without CD by key clinical parameters (age by deciles, gender, no early revascularization, pre-test likelihood categories, diabetes, and chest pain symptoms). Multivariable analysis of stress MPS total perfusion deficit (STPD) and major clinical confounders were used as predictors of CD. Visual 17-segment summed stress segmental scores (VSSS) obtained by an expert reader, were compared to STPD. RESULTS: CD patients had higher stress hypoperfusion measures compared to controls [STPD: 7.0% vs 3.6% (P < .05), VSSS: 5.3 vs 2.1 (P < .05)]. By univariate analysis, STPD and VSSS have similar predictive power (the areas under receiver operator characteristics curves: STPD = 0.64, VSSS = 0.63; Kaplan-Meier models: χ(2) = 7.59, P = .0059 for STPD and χ(2) = 11.10, P = .0009 for VSSS). The multiple Cox proportional hazards regression models with continuous perfusion measures showed that STPD had similar power to normalized VSSS as a predictor for CD (χ(2) = 4.92; P = .027) vs (χ(2) = 8.90; P = .003). CONCLUSIONS: Quantitative analysis is comparable to expert visual scoring in predicting CD in a case-controlled study.

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence.

OBJECTIVE: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). METHODS: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years. RESULTS: The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p<0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. CONCLUSION: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.