Pieter H. Kitslaar

Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms.

Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available, and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.

Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification

AIMS Previous studies have used semi-automated approaches for coronary plaque quantification on multi-detector row computed tomography (CT), while an automated quantitative approach using a dedicated registration algorithm is currently lacking. Accordingly, the study aimed to demonstrate the feasibility and accuracy of automated coronary plaque quantification on cardiac CT using dedicated software with a novel 3D coregistration algorithm of CT and intravascular ultrasound (IVUS) data sets. METHODS AND RESULTS Patients who had undergone CT and IVUS were enrolled. Automated lumen and vessel wall contour detection was performed for both imaging modalities. Dedicated automated quantitative software (QCT) with a unique registration algorithm was used to fuse a complete IVUS run with a CT angiography volume using true anatomical markers. At the level of the minimal lumen area (MLA), percentage lumen area stenosis, plaque burden, and degree of remodelling were obtained on CT. Additionally, mean plaque burden was assessed for the whole coronary plaque. At the identical level within the coronary artery, the same variables were derived from IVUS. Fifty-one patients (40 men, 58 ± 11 years, 103 coronary arteries) with 146 lesions were evaluated. Quantitative computed tomography and IVUS showed good correlation for MLA (n = 146, r = 0.75, P < 0.001). At the level of the MLA, both techniques were well-correlated for lumen area stenosis (n = 146, r = 0.79, P < 0.001) and plaque burden (n = 146, r = 0.70, P < 0.001). Mean plaque burden (n = 146, r = 0.64, P < 0.001) and remodelling index (n = 146, r = 0.56, P < 0.001) showed significant correlations between QCT and IVUS. CONCLUSION Automated quantification of coronary plaque on CT is feasible using dedicated quantitative software with a novel 3D registration algorithm.

Diagnostic performance of hyperaemic myocardial blood flow index obtained by dynamic computed tomography: does it predict functionally significant coronary lesions?

AIMS The severity of coronary artery narrowing is a poor predictor of functional significance, in particular in intermediate coronary lesions (30-70% diameter narrowing). The aim of this work was to compare the performance of a quantitative hyperaemic myocardial blood flow (MBF) index derived from adenosine dynamic computed tomography perfusion (CTP) imaging with that of visual CT coronary angiography (CTCA) and semi-automatic quantitative CT (QCT) in the detection of functionally significant coronary lesions in patients with stable chest pain. METHODS AND RESULTS CTCA and CTP were performed in 80 patients (210 analysable coronary vessels) referred to invasive coronary angiography (ICA). The MBF index (mL/100 mL/min) was computed using a model-based parametric deconvolution method. The diagnostic performance of the MBF index in detecting functionally significant coronary lesions was compared with visual CTCA and QCT. Coronary lesions with invasive fractional flow reserve of ≤0.75 were defined as functionally significant. The optimal cut-off value of the MBF index to detect functionally significant coronary lesions was 78 mL/100 mL/min. On a vessel-territory level, the MBF index had a larger area under the curve (0.95; 95% confidence interval [95% CI]: 0.92-0.98) compared with visual CTCA (0.85; 95% CI: 0.79-0.91) and QCT (0.89; 95% CI: 0.84-0.93) (both P-values <0.001). In the analysis restricted to intermediate coronary lesions, the specificity of visual CTCA (69%) and QCT (77%) could be improved by the subsequent use of the MBF index (89%). CONCLUSION In this proof-of-principle study, the MBF index performed better than visual CTCA and QCT in the identification of functionally significant coronary lesions. The MBF index had additional value beyond CTCA anatomy in intermediate coronary lesions. This may have a potential to support patient management.

Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with experts manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

Natural history of coronary atherosclerosis by multislice computed tomography.

OBJECTIVES This study sought to analyze the natural history of coronary atherosclerosis by multislice computed tomography (MSCT) and assess the serial changes in coronary plaque burden, lumen dimensions, and arterial remodeling. BACKGROUND MSCT can comprehensively assess coronary atherosclerosis by combining lumen and plaque size parameters. METHODS Thirty-two patients with acute coronary syndromes underwent 64-slice computed tomography angiography after percutaneous coronary intervention at baseline and after a median of 39 months. All patients received contemporary medical treatment. All available coronary segments in every subject were analyzed. The progression of atherosclerosis per segment and per patient was assessed by means of change in percent atheroma volume (PAV), change in normalized total atheroma volume (TAVnorm), and percent change in TAV (% change in TAV). Serial coronary remodeling was also assessed. Measures of lumen stenosis included percent diameter stenosis (%DS), minimum lumen diameter (MLD), percent area stenosis (%AS), and minimum lumen area (MLA). For each patient, the mean of all matched segments was calculated at the 2 time points. Clinical events at follow-up were documented. RESULTS The PAV did not change significantly (-0.15 ± 3.64%, p = 0.72). The mean change in TAVnorm was 47.36 ± 143.24 mm(3) (p = 0.071), and the % change in TAV was 6.7% (p = 0.029). The MLD and MLA increased by 0.15 mm (-0.09 to 0.24, p = 0.039) and 0.52 mm(2) (-0.38 to 1.04, p = 0.034) respectively, which was accompanied by vessel enlargement, with 53% of the patients showing expansive positive remodeling. Patients with clinical events had a larger TAVnorm at baseline (969.72 mm(3) vs. 810.77 mm(3), p = 0.010). CONCLUSIONS MSCT can assess the progression of coronary atherosclerosis and may be used for noninvasive monitoring of pharmacological interventions in coronary artery disease. ( PROSPECT An Imaging Study in Patients With Unstable Atherosclerotic Lesions; NCT00180466).

Automated quantification of stenosis severity on 64-slice CT: a comparison with quantitative coronary angiography.

OBJECTIVES This study sought to demonstrate the feasibility of a dedicated algorithm for automated quantification of stenosis severity on multislice computed tomography in comparison with quantitative coronary angiography (QCA). BACKGROUND Limited information is available on quantification of coronary stenosis, and previous attempts using semiautomated approaches have been suboptimal. METHODS In patients who had undergone 64-slice computed tomography and invasive coronary angiography, the most severe lesion on QCA was quantified per coronary artery using quantitative coronary computed tomography (QCCTA) software. Additionally, visual grading of stenosis severity using a binary approach (50% stenosis as a cutoff) was performed. Diameter stenosis (percentage) was obtained from detected lumen contours at the minimal lumen area, and corresponding reference diameter values were obtained from an automatic trend analysis of the vessel areas within the artery. RESULTS One hundred patients (53 men; 59.8 +/- 8.0 years) were evaluated, and 282 (94%) vessels were analyzed. Good correlations for diameter stenosis were observed for vessel-based (n = 282; r = 0.83; p < 0.01) and patient-based (n = 93; r = 0.86; p < 0.01) analyses. Mean differences between QCCTA and QCA were -3.0% +/- 12.3% and -6.2% +/- 12.4%. Furthermore, good agreement was observed between QCCTA and QCA for semiquantitative assessment of diameter stenosis (accuracy of 95%). Diagnostic accuracy for assessment of > or =50% diameter stenosis was higher using QCCTA compared with visual analysis (95% vs. 87%; p = 0.08). Moreover, a significantly higher positive predictive value was observed with QCCTA when compared with visual analysis (100% vs. 78%; p < 0.05). Although the visual approach showed a reduced diagnostic accuracy for data sets with moderate image quality, QCCTA performed equally well in patients with moderate or good image quality. However, in data sets with good image quality, QCCTA tended to have a reduced sensitivity compared with visual analysis. CONCLUSIONS Good correlations were found for quantification of stenosis severity between QCCTA and QCA. QCCTA showed an improved positive predictive value when compared with visual analysis.

Quantitative Computed Tomographic Coronary Angiography Does It Predict Functionally Significant Coronary Stenoses

Background—Coronary lesions with a diameter narrowing ≥50% on visual computed tomographic coronary angiography (CTCA) are generally considered for referral to invasive coronary angiography. However, similar to invasive coronary angiography, visual CTCA is often inaccurate in detecting functionally significant coronary lesions. We sought to compare the diagnostic performance of quantitative CTCA with visual CTCA for the detection of functionally significant coronary lesions using fractional flow reserve (FFR) as the reference standard. Methods and Results—CTCA and FFR measurements were obtained in 99 symptomatic patients. In total, 144 coronary lesions detected on CTCA were visually graded for stenosis severity. Quantitative CTCA measurements included lesion length, minimal area diameter, % area stenosis, minimal lumen diameter, % diameter stenosis, and plaque burden [(vessel area−lumen area)/vessel area×100]. Optimal cutoff values of CTCA-derived parameters were determined, and their diagnostic accuracy for the detection of flow-limiting coronary lesions (FFR ⩽0.80) was compared with visual CTCA. FFR was ⩽0.80 in 54 of 144 (38%) coronary lesions. Optimal cutoff values to predict flow-limiting coronary lesion were 10 mm for lesion length, 1.8 mm2 for minimal area diameter, 73% for % area stenosis, 1.5 mm for minimal lumen diameter, 48% for % diameter stenosis, and 76% for plaque burden. No significant difference in sensitivity was found between visual CTCA and quantitative CTCA parameters (P>0.05). The specificity of visual CTCA (42%; 95% confidence interval [CI], 31%–54%) was lower than that of minimal area diameter (68%; 95% CI, 57%–77%; P=0.001), % area stenosis (76%; 95% CI, 65%–84%; P<0.001), minimal lumen diameter (67%; 95% CI, 55%–76%; P=0.001), % diameter stenosis (72%; 95% CI, 62%–80%; P<0.001), and plaque burden (63%; 95% CI, 52%–73%; P=0.004). The specificity of lesion length was comparable with that of visual CTCA. Conclusions—Quantitative CTCA improves the prediction of functionally significant coronary lesions compared with visual CTCA assessment but remains insufficient. Functional assessment is still required in lesions of moderate stenosis to accurately detect impaired FFR.

Quantitative CT Coronary Angiography: Does It Predict Functionally Significant Coronary Stenoses?

Background—Coronary lesions with a diameter narrowing ≥50% on visual computed tomographic coronary angiography (CTCA) are generally considered for referral to invasive coronary angiography. However, similar to invasive coronary angiography, visual CTCA is often inaccurate in detecting functionally significant coronary lesions. We sought to compare the diagnostic performance of quantitative CTCA with visual CTCA for the detection of functionally significant coronary lesions using fractional flow reserve (FFR) as the reference standard. Methods and Results—CTCA and FFR measurements were obtained in 99 symptomatic patients. In total, 144 coronary lesions detected on CTCA were visually graded for stenosis severity. Quantitative CTCA measurements included lesion length, minimal area diameter, % area stenosis, minimal lumen diameter, % diameter stenosis, and plaque burden [(vessel area−lumen area)/vessel area×100]. Optimal cutoff values of CTCA-derived parameters were determined, and their diagnostic accuracy for the detection of flow-limiting coronary lesions (FFR ⩽0.80) was compared with visual CTCA. FFR was ⩽0.80 in 54 of 144 (38%) coronary lesions. Optimal cutoff values to predict flow-limiting coronary lesion were 10 mm for lesion length, 1.8 mm2 for minimal area diameter, 73% for % area stenosis, 1.5 mm for minimal lumen diameter, 48% for % diameter stenosis, and 76% for plaque burden. No significant difference in sensitivity was found between visual CTCA and quantitative CTCA parameters (P>0.05). The specificity of visual CTCA (42%; 95% confidence interval [CI], 31%–54%) was lower than that of minimal area diameter (68%; 95% CI, 57%–77%; P=0.001), % area stenosis (76%; 95% CI, 65%–84%; P<0.001), minimal lumen diameter (67%; 95% CI, 55%–76%; P=0.001), % diameter stenosis (72%; 95% CI, 62%–80%; P<0.001), and plaque burden (63%; 95% CI, 52%–73%; P=0.004). The specificity of lesion length was comparable with that of visual CTCA. Conclusions—Quantitative CTCA improves the prediction of functionally significant coronary lesions compared with visual CTCA assessment but remains insufficient. Functional assessment is still required in lesions of moderate stenosis to accurately detect impaired FFR.

Computed tomography-based high-risk coronary plaque score to predict acute coronary syndrome among patients with acute chest pain--Results from the ROMICAT II trial.

BACKGROUND Coronary computed tomography angiography (CTA) can be used to detect and quantitatively assess high-risk plaque features. OBJECTIVE To validate the ROMICAT score, which was derived using semi-automated quantitative measurements of high-risk plaque features, for the prediction of ACS. MATERIAL AND METHODS We performed quantitative plaque analysis in 260 patients who presented to the emergency department with suspected ACS in the ROMICAT II trial. The readers used a semi-automated software (QAngio, Medis medical imaging systems BV) to measure high-risk plaque features (volume of <60HU plaque, remodeling index, spotty calcium, plaque length) and diameter stenosis in all plaques. We calculated a ROMICAT score, which was derived from the ROMICAT I study and applied to the ROMICAT II trial. The primary outcome of the study was diagnosis of an ACS during the index hospitalization. RESULTS Patient characteristics (age 57 ± 8 vs. 56 ± 8 years, cardiovascular risk factors) were not different between those with and without ACS (prevalence of ACS 7.8%). There were more men in the ACS group (84% vs. 59%, p = 0.005). When applying the ROMICAT score derived from the ROMICAT I trial to the patient population of the ROMICAT II trial, the ROMICAT score (OR 2.9, 95% CI 1.4-6.0, p = 0.003) was a predictor of ACS after adjusting for gender and ≥ 50% stenosis. The AUC of the model containing ROMICAT score, gender, and ≥ 50% stenosis was 0.91 (95% CI 0.86-0.96) and was better than with a model that included only gender and ≥ 50% stenosis (AUC 0.85, 95%CI 0.77-0.92; p = 0.002). CONCLUSIONS The ROMICAT score derived from semi-automated quantitative measurements of high-risk plaque features was an independent predictor of ACS during the index hospitalization and was incremental to gender and presence of ≥ 50% stenosis.

Clinical Feasibility of 3D Automated Coronary Atherosclerotic Plaque Quantification Algorithm on Coronary Computed Tomography Angiography: Comparison with Intravascular Ultrasound.

ObjectiveTo evaluate the diagnostic performance of automated coronary atherosclerotic plaque quantification (QCT) by different users (expert/non-expert/automatic).MethodsOne hundred fifty coronary artery segments from 142 patients who underwent coronary computed tomography angiography (CCTA) and intravascular ultrasound (IVUS) were analyzed. Minimal lumen area (MLA), maximal lumen area stenosis percentage (%AS), mean plaque burden percentage (%PB), and plaque volume were measured semi-automatically by expert, non-expert, and fully automatic QCT analyses, and then compared to IVUS.ResultsBetween IVUS and expert QCT analysis, the correlation coefficients (r) for the MLA, %AS, %PB, and plaque volume were excellent: 0.89 (p < 0.001), 0.84 (p < 0.001), 0.91 (p < 0.001), and 0.94 (p < 0.001), respectively. There were no significant differences in the mean parameters (all p values >0.05) except %AS (p = 0.01). The automatic QCT analysis showed comparable performance to non-expert QCT analysis, showing correlation coefficients (r) of the MLA (0.80 vs. 0.82), %AS (0.82 vs. 0.80), %PB (0.84 vs. 0.73), and plaque volume (0.84 vs. 0.79) when they were compared to IVUS, respectively.ConclusionFully automatic QCT analysis showed clinical utility compared with IVUS, as well as a compelling performance when compared with semiautomatic analyses.Key Points• Coronary CTA enables the assessment of coronary atherosclerotic plaque.• High-risk plaque characteristics and overall plaque burden can predict future cardiac events.• Coronary atherosclerotic plaque quantification is currently unfeasible in practice.• Quantitative computed tomography coronary plaque analysis software (QCT) enables feasible plaque quantification.• Fully automatic QCT analysis shows excellent performance.