Alexander Broersen

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.

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.

Automated, Feature-Based Image Alignment for High-Resolution Imaging Mass Spectrometry of Large Biological Samples

High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment routine that uses principal component analysis to reduce the uncorrelated noise in the imaging datasets, which previously obstructed automated image alignment. An additional signal quality metric ensures that only those regions with sufficient signal quality are considered. We demonstrate that this algorithm provides superior alignment performance than manual stitching and can be used to automatically align large imaging mass spectrometry datasets comprising many individual mosaic tiles.

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.

Transfer functions for imaging spectroscopy data using principal component analysis

In this paper we present a new application of the principal component analysis (PCA) to generate multidimensional transfer functions. These transfer functions are needed in the volumetric visualization of spectral data to isolate regions that contain interesting peak-shaped features. Both large and small peaks can be equally important and represent the presence of different chemical elements in a dataset. Principal component analysis separates these peaks in different uncorrelated components and can simultaneously identify spatial patterns. This approach is characterized by the direct linkage between the resulting spectral and spatial components. Our method enables us to create an opacity map from these components. One or more mappings can be selected to highlight features in three-dimensional volume visualization.

Feasibility of an automated quantitative computed tomography angiography-derived risk score for risk stratification of patients with suspected coronary artery disease.

Coronary computed tomography angiography (CTA) has important prognostic value. Additionally, quantitative CTA (QCT) provides a more detailed accurate assessment of coronary artery disease (CAD) on CTA. Potentially, a risk score incorporating all quantitative stenosis parameters allows accurate risk stratification. Therefore, the purpose of this study was to determine if an automatic quantitative assessment of CAD using QCT combined into a CTA risk score allows risk stratification of patients. In 300 patients, QCT was performed to automatically detect and quantify all lesions in the coronary tree. Using QCT, a novel CTA risk score was calculated based on plaque extent, severity, composition, and location on a segment basis. During follow-up, the composite end point of all-cause mortality, revascularization, and nonfatal infarction was recorded. In total, 10% of patients experienced an event during a median follow-up of 2.14 years. The CTA risk score was significantly higher in patients with an event (12.5 [interquartile range 8.6 to 16.4] vs 1.7 [interquartile range 0 to 8.4], p <0.001). In 127 patients with obstructive CAD (≥50% stenosis), 27 events were recorded, all in patients with a high CTA risk score. In conclusion, the present study demonstrated that a fully automatic QCT analysis of CAD is feasible and can be applied for risk stratification of patients with suspected CAD. Furthermore, a novel CTA risk score incorporating location, severity, and composition of coronary lesion was developed. This score may improve risk stratification but needs to be confirmed in larger studies.

Aged Garlic Extract Reduces Low Attenuation Plaque in Coronary Arteries of Patients with Metabolic Syndrome in a Prospective Randomized Double-Blind Study

BACKGROUND Although several previous studies have demonstrated that aged garlic extract (AGE) inhibits the progression of coronary artery calcification, its effect on noncalcified plaque (NCP) has been unclear. OBJECTIVE This study investigated whether AGE reduces coronary plaque volume measured by cardiac computed tomography angiography (CCTA) in patients with metabolic syndrome (MetS). METHODS Fifty-five patients with MetS (mean ± SD age: 58.7 ± 6.7 y; 71% men) were prospectively assigned to consume 2400 mg AGE/d (27 patients) or placebo (28 patients) orally. Both groups underwent CCTA at baseline and follow-up 354 ± 41 d apart. Coronary plaque volume, including total plaque volume (TPV), dense calcium (DC), NCP, and low-attenuation plaque (LAP), were measured based upon predefined intensity cutoff values. Multivariable linear regression analysis, adjusted for age, gender, number of risk factors, hyperlipidemia medications, history of coronary artery disease, scan interval time, and baseline %TPV, was performed to examine whether AGE affected each plaque change. RESULTS The %LAP change was significantly reduced in the AGE group compared with the placebo group (-1.5% ± 2.3% compared with 0.2% ± 2.0%, P = 0.0049). In contrast, no difference was observed in %TPV change (0.3% ± 3.3% compared with 1.6% ± 3.0%, P = 0.13), %NCP change (0.2% ± 3.3% compared with 1.4% ± 2.9%, P = 0.14), and %DC change (0.2% ± 1.4%, compared with 0.2% ± 1.7%, P = 0.99). Multivariable linear regression analysis found a beneficial effect of AGE on %LAP regression (β: -1.61; 95% CI: -2.79, -0.43; P = 0.008). CONCLUSIONS This study indicates that the %LAP change was significantly greater in the AGE group than in the placebo group. Further studies are needed to evaluate whether AGE has the ability to stabilize vulnerable plaque and decrease adverse cardiovascular events. This trial was registered at clinicaltrials.gov as NCT01534910.