Pericoronary Adipose Tissue Attenuation in Patients With Acute Coronary Syndrome Versus Stable Coronary Artery Disease.

BACKGROUND: Pericoronary adipose tissue (PCAT) attenuation has been associated with coronary inflammation and can be evaluated with coronary computed tomography angiography. The aims of this study were to compare the PCAT attenuation across precursors of culprit and nonculprit lesions of patients with acute coronary syndrome versus stable coronary artery disease (CAD). METHODS: In this case-control study, patients with suspected CAD who underwent coronary computed tomography angiography were included. Patients who developed an acute coronary syndrome within 2 years after the coronary computed tomography angiography scan were identified, and patients with stable CAD (defined as any coronary plaque ≥30% luminal diameter stenosis) were 1:2 propensity score matched for age, sex, and cardiac risk factors. The mean PCAT attenuation was analyzed at lesion level and compared between precursors of culprit lesions, nonculprit lesions, and stable coronary plaques. RESULTS: In total, 198 patients (age 62±10 years, 65% male) were selected, including 66 patients who developed an acute coronary syndrome and 132 propensity matched patients with stable CAD. Overall, 765 coronary lesions were analyzed (culprit lesion precursors: n=66; nonculprit lesion precursors: n=207; and stable lesions: n=492). Culprit lesion precursors had larger total plaque volume, fibro-fatty plaque volume, and low-attenuation plaque volume compared to nonculprit and stable lesions. The mean PCAT attenuation was significantly higher across culprit lesion precursors compared to nonculprit and stable lesions (-63.8±9.7 Hounsfield units versus -68.8±10.6 Hounsfield units versus -69.6±10.6 Hounsfield units, respectively; P<0.001), whereas the mean PCAT attenuation around nonculprit and stable lesions was not significantly different (P=0.99). CONCLUSIONS: The mean PCAT attenuation is significantly increased across culprit lesion precursors in patients with acute coronary syndrome, compared to nonculprit lesions of these patients and to lesions of patients with stable CAD, which may suggest a higher intensity of inflammation. PCAT attenuation on coronary computed tomography angiography may be a novel marker to identify high-risk plaques.

Vessel and sex differences in pericoronary adipose tissue attenuation obtained with coronary CT in individuals without coronary atherosclerosis.

Pericoronary adipose tissue (PCAT) attenuation, derived from coronary computed tomography angiography (CCTA), is associated with coronary artery inflammation. Values for PCAT attenuation in men and women without atherosclerosis on CCTA are lacking. The aim of the current study was to assess the mean PCAT attenuation in individuals without coronary artery atherosclerosis on CCTA. Data on PCAT attenuation in men and women without coronary artery atherosclerosis on CCTA were included in this retrospective analysis. The PCAT attenuation was analyzed from the proximal part of the right coronary artery (RCA), the left anterior descending artery (LAD), and the left circumflex artery (LCx). For patient level analyses the mean PCAT attenuation was defined as the mean of the three coronary arteries. In 109 individuals (mean age 45 ± 13 years; 44% men), 320 coronary arteries were analyzed. The mean PCAT attenuation of the overall population was - 64.4 ± 8.0 HU. The mean PCAT attenuation was significantly lower in the LAD compared with the LCx and RCA (- 67.8 ± 7.8 HU vs - 62.6 ± 6.8 HU vs - 63.6 ± 7.9 HU, respectively, p < 0.001). In addition, the mean PCAT attenuation was significantly higher in men vs. women in all three coronary arteries (LAD: - 65.7 ± 7.6 HU vs - 69.4 ± 7.6 HU, p = 0.014; LCx: - 60.6 ± 7.4 HU vs - 64.3 ± 5.9 HU, p = 0.008; RCA: -61.7 ± 7.9 HU vs - 65.0 ± 7.7 HU, p = 0.029, respectively). The current study provides mean PCAT attenuation values, derived from individuals without CAD. Moreover, the mean PCAT attenuation is lower in women vs. men. Furthermore, the mean PCAT attenuation is significantly lower in the LAD vs LCx and RCA.

Association Between Changes in Perivascular Adipose Tissue Density and Plaque Progression.

BACKGROUND: The association between the change in vessel inflammation, as quantified by perivascular adipose tissue (PVAT) density, and the progression of coronary atherosclerosis remains to be determined. OBJECTIVES: The purpose of this study was to explore the association between the change in PVAT density and the progression of total and compositional plaque volume (PV). METHODS: Patients were selected from a prospective multinational registry. Patients who underwent serial coronary computed tomography angiography studies with ≥2-year intervals and were scanned with the same tube voltage at baseline and follow-up were included. Total and compositional PV and PVAT density at baseline and follow-up were quantitatively analyzed for every lesion. Multivariate linear regression models using cluster analyses were constructed. RESULTS: A total of 1,476 lesions were identified from 474 enrolled patients (mean age 61.2 ± 9.3 years; 65.0% men). The mean PVAT density was -74.1 ± 11.5 HU, and total PV was 48.1 ± 83.5 mm3 (19.2 ± 44.8 mm3 of calcified PV and 28.9 ± 51.0 mm3 of noncalcified PV). On multivariate analysis (adjusted for clinical risk factors, medication use, change in lipid levels, total PV at baseline, luminal HU attenuation, location of lesions, and tube voltage), the increase in PVAT density was positively associated with the progression of total PV (estimate = 0.275 [95% CI: 0.004-0.545]; P = 0.047), driven by the association with fibrous PV (estimate = 0.245 [95% CI: 0.070-0.420]; P = 0.006). Calcified PV progression was not associated with the increase in PVAT density (P > 0.050). CONCLUSIONS: Increase in vessel inflammation represented by PVAT density is independently associated with the progression of the lipid component of coronary atherosclerotic plaques. (Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging [PARADIGM]; NCT02803411).

Comparative effectiveness of coronary artery stenosis and atherosclerotic plaque burden assessment for predicting 30-day revascularization and 2-year major adverse cardiac events.

PURPOSE: To provide comparative prognostic information of coronary atherosclerotic plaque volume and stenosis assessment in patients with suspected coronary artery disease (CAD). METHODS: We followed 372 patients with suspected or known CAD enrolled in the CORE320 study for 2 years after baseline 320-detector row cardiac CT scanning and invasive quantitative coronary angiography (QCA). CT images were analyzed for coronary calcium scanning (CACS), semi-automatically derived total percent atheroma volume (PAV), segment stenosis score (SSS), in addition to traditional stenosis assessment (≥ 50%) by CT and QCA for (1) 30-day revascularization and (2) major adverse cardiac events (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare accuracy of risk prediction. RESULTS: Sixty percent of patients had obstructive CAD by QCA with 23% undergoing 30-day revascularization and 9% experiencing MACE at 2 years. Most late events (20/32) were revascularization procedures. Prediction of 30-day revascularization was modest (AUC range 0.67-0.78) but improved after excluding patients with known CAD (AUC range 0.73-0.86, p < 0.05 for all). Similarly, prediction of MACE improved after excluding patients with known CAD (AUC range 0.58-0.73 vs. 0.63-0.77). CT metrics of atherosclerosis burden performed overall similarly but stenosis assessment was superior for predicting 30-day revascularization. CONCLUSIONS: Angiographic and coronary atherosclerotic plaque metrics perform only modestly well for predicting 30-day revascularization and 2-year MACE in high risk patients but improve after excluding patients with known CAD. Atherosclerotic plaque metrics did not yield incremental value over stenosis assessment for predicting events that predominantly consisted of revascularization procedures. CLINICAL TRIAL REGISTRATION: NCT00934037.

Automatic coronary artery plaque thickness comparison between baseline and follow-up CCTA images.

PURPOSE: Currently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time. METHODS: We model the lumen and vessel wall for both the baseline coronary artery tree (CAT-BL) and follow-up coronary artery tree (CAT-FU) as smooth three-dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT-FU to CAT-BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT-BL and CAT-FU pairs were created to simulate the plaque decrease and increase over time. RESULTS: For 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95 ± 0.98 mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation ± 0.1 mm). CONCLUSIONS: The proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison.

Apixaban versus warfarin in evaluation of progression of atherosclerotic and calcified plaques (prospective randomized trial).

Warfarin has been showed to increase vascular calcification. Apixaban, a direct factor Xa inhibitor, has no interaction with vitamin K and its effect on coronary plaques is unknown. We randomized and compared warfarin and apixaban on progression of coronary atherosclerotic plaques measured by coronary computed tomographic angiography in 66 subjects with non-valvular atrial fibrillation over the period of one-year follow up. There was significant higher total, calcified and low attenuation plaque volume in the group randomized to warfarin as compared to apixaban (all P < .05). Greater volume of total (ß2 = 28.54; P = .03), low attenuation plaque (ß2 = 3.58; P = .02) and calcified (ß2 = 14.10; P = .005) plaque progression was observed in the VKA_group.

A novel alignment procedure to assess calcified coronary plaques in histopathology, post-mortem computed tomography angiography and optical coherence tomography.

PURPOSE: Improve mapping and registration of longitudinal view on histopathology vessels in a three-dimensional alignment procedure for postmortem quantitative coronary plaque analyses. This new procedure is applied and results shown using calcified coronary plaque analyses within post-mortem computed tomography angiography (PMCTA), optical coherence tomography (OCT) and the gold standard of histopathology. RESULTS: In total, 338 annotated histopathology images were included, 166 PMCTA transversal images and 285 OCT images were aligned in the comparison. The results from the comparison using the alignment procedure showed overall that the calcified plaques seem to be overestimated by PMCTA and underestimated by OCT. CONCLUSIONS: The 3D fusion approach, aligning the images of PMCTA, OCT and histopathology as gold standard allowed for a slice-based comparison of the different modalities. The results showed that PMCTA overestimates the calcified plaques while OCT underestimates these, compared to histopathology.

Comparative Effectiveness of CT-Derived Atherosclerotic Plaque Metrics for Predicting Myocardial Ischemia.

OBJECTIVES: This study sought to investigate the performance of various cardiac computed tomography (CT)-derived atherosclerotic plaque metrics for predicting provocable myocardial ischemia. BACKGROUND: The association of coronary arterial diameter stenosis with myocardial ischemia is only modest, but cardiac CT provides several other, readily available atherosclerosis metrics, which may have incremental value. METHODS: The study analyzed 873 nonstented coronary arteries and their myocardial perfusion territories in 356 patients (mean 62 years of age) enrolled in the CORE320 (Coronary Artery Evaluation using 320-row Multidetector Computed Tomography Angiography and Myocardial Perfusion) study. Myocardial perfusion defects in static CT perfusion imaging were graded at rest and after adenosine in 13 myocardial segments using a 4-point scale. The summed difference score was calculated by subtracting the summed rest score from the summed stress score. Reversible ischemia was defined as summed difference score ≥1. In a sensitivity analysis, results were also provided using single-photon emission computed tomography (SPECT) as the reference standard. Vessel based predictor variables included maximum percent diameter stenosis, lesion length, coronary calcium score, maximum cross-sectional calcium arc, percent atheroma volume (PAV), low-attenuation atheroma volume, positive (external) vascular remodeling, and subjective impression of "vulnerable plaque." The study used logistic regression models to assess the association of plaque metrics with myocardial ischemia. RESULTS: In univariate analysis, all plaque metrics were associated with reversible ischemia. In the adjusted logistic model, only maximum percent diameter stenosis (1.26; 95% confidence interval: 1.15 to 1.38) remained an independent predictor. With SPECT as outcome variable, PAV and "vulnerable" plaque remained predictive after adjustment. In vessels with intermediate stenosis (40% to 70%), no single metric had clinically meaningful incremental value. CONCLUSIONS: Various plaque metrics obtained by cardiac CT predict provocable myocardial ischemia by CT perfusion imaging through their association with maximum percent stenosis, while none had significant incremental value. With SPECT as reference standard, PAV and "vulnerable plaque" remained predictors of ischemia after adjustment but the predictive value added to stenosis assessment alone was small.

A model-guided method for improving coronary artery tree extractions from CCTA images.

PURPOSE: Automatically extracted coronary artery trees (CATs) from coronary computed tomography angiography images could contain incorrect extractions which require manual corrections before they can be used in clinical practice. A model-guided method for improving the extracted CAT is described to automatically detect potential incorrect extractions and improve them. METHODS: The proposed method is a coarse-to-fine approach. A coarse improvement is first applied on all vessels in the extracted CAT, and then a fine improvement is applied only on vessels with higher clinical significance. Based upon a decision tree, the proposed method automatically and iteratively performs improvement operations for the entire extracted CAT until it meets the stop criteria. The improvement in the extraction quality obtained by the proposed method is measured using a scoring system. 18 datasets were used to determine optimal values for the parameters involved in the model-guided method and 122 datasets were used for evaluation. RESULTS: Compared to the initial automatic extractions, the proposed method improves the CATs for 122 datasets from an average quality score of 87 ± 6 to 93 ± 4. The developed method is able to run within 2 min on a typical workstation. The difference in extraction quality after automatic improvement is negatively correlated with the initial extraction quality (R = - 0.694, P < 0.001). CONCLUSION: Without deteriorating the initially extracted CATs, the presented method automatically detects incorrect extractions and improves the CATs to an average quality score of 93 guided by anatomical statistical models.

Impact of diabetes on coronary artery plaque volume by coronary CT angiography and subsequent adverse cardiac events.

BACKGROUND: To investigate the impact of diabetes on coronary artery total plaque volume (TPV) and adverse events in long-term follow-up. METHODS: One-hundred-and-eight diabetic patients were matched to 324 non-diabetic patients, with respect to age, sex, body-mass index, hypertension, smoking habits, LDL and HDL cholesterol, family history for CAD as well as aspirin and statin medication. In all patients, TPV was quantified from coronary CT angiographies (CTA) using dedicated software. All-cause mortality, acute coronary syndrome and late revascularisation (>90 days) served as combined endpoint. RESULTS: Patients were followed for 5.6 years. The endpoint occurred in 18 (16.7%) diabetic and 26 (8.0%) non-diabetic patients (odds ratio 2.3, p = 0.03). Diabetic patients had significantly higher TPV than non-diabetic patients (55.1 mm³ [IQR: 6.2 and 220.4 mm³] vs. 24.9 mm³ [IQR: 0 and 166.7 mm³], p = 0.02). A TPV threshold of 110.5 mm³ provided good separation of diabetic and non-diabetic patients at higher and lower risk for adverse events. Noteworthy, diabetic and non-diabetic patients with a TPV<110.5 mm³ had comparable outcome (hazard ratio: 1.3, p = 0.59), while diabetic patients with TPV>110.5 mm³ had significantly higher incidence of adverse events (hazard ratio 2.3, p = 0.03) compared to non-diabetic patients with TPV>110.5 mm³. There was incremental prognostic value in diabetic and non-diabetic patients over the Framingham Risk Score (Integrated Discrimination Improvement: 0.052 and 0.012, p for both <0.05). CONCLUSION: Diabetes is associated with significantly higher TPV, which is independent of other CAD risk factors. Quantification of TPV improves the identification of diabetic patients at higher risk for future adverse events.

A novel software tool for semi-automatic quantification of thoracic aorta dilatation on baseline and follow-up computed tomography angiography.

A dedicated software package that could semi-automatically assess differences in aortic maximal cross-sectional diameters from consecutive CT scans would most likely reduce the post-processing time and effort by the physicians. The aim of this study was to present and assess the quality of a new tool for the semi-automatic quantification of thoracic aorta dilation dimensions. Twenty-nine patients with two CTA scans of the thoracic aorta for which the official clinical report indicated an increase in aortic diameters were included in the study. Aortic maximal cross-sectional diameters of baseline and follow-up studies generated semi-automatically by the software were compared with corresponding manual measurements. The semi-automatic measurements were performed at seven landmarks defined on the baseline scan by two operators. Bias, Bland-Altman plots and intraclass correlation coefficients were calculated between the two methods and, for the semi-automatic software, also between two observers. The average time difference between the two scans of a single patient was 1188 ± 622 days. For the semi-automatic software, in 2 out of 29 patients, manual interaction was necessary; in the remaining 27 patients (93.1%), semi-automatic results were generated, demonstrating excellent intraclass correlation coefficients (all values ≥ 0.91) and small differences, especially for the proximal aortic arch (baseline: 0.19 ± 1.30 mm; follow-up: 0.44 ± 2.21 mm), the mid descending aorta (0.37 ± 1.64 mm; 0.37 ± 2.06 mm), and the diaphragm (0.30 ± 1.14 mm; 0.37 ± 1.80 mm). The inter-observer variability was low with all errors in diameters ≤ 1 mm, and intraclass correlation coefficients all ≥ 0.95. The semi-automatic tool decreased the processing time by 40% (13 vs. 22 min). In this work, a semi-automatic software package that allows the assessment of thoracic aorta diameters from baseline and follow-up CTs (and their differences), was presented, and demonstrated high accuracy and low inter-observer variability.

Coronary Computed Tomography Angiography-Specific Definitions of High-Risk Plaque Features Improve Detection of Acute Coronary Syndrome.

Background High-risk plaque (HRP) features as detected by coronary computed tomography angiography (CTA) predict acute coronary syndrome (ACS). We sought to determine whether coronary CTA-specific definitions of HRP improve discrimination of patients with ACS as compared with definitions from intravascular ultrasound (IVUS). Methods and Results In patients with suspected ACS, randomized to coronary CTA in the ROMICAT II (Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography II) trial, we retrospectively performed semiautomated quantitative analysis of HRP (including remodeling index, plaque burden as derived by plaque area, low computed tomography attenuation plaque volume) and degree of luminal stenosis and analyzed the performance of traditional IVUS thresholds to detect ACS. Furthermore, we derived CTA-specific thresholds in patients with ACS to detect culprit lesions and applied those to all patients to calculate the discriminatory ability to detect ACS in comparison to IVUS thresholds. Of 472 patients, 255 patients (56±7.8 years; 63% men) had coronary plaque. In 32 patients (6.8%) with ACS, culprit plaques (n=35) differed from nonculprit plaques (n=172) with significantly greater values for all HRP features except minimal luminal area (significantly lower; all P<0.01). IVUS definitions showed good performance while minimal luminal area (odds ratio: 6.82; P=0.014) and plaque burden (odds ratio: 5.71; P=0.008) were independently associated with ACS but not remodeling index (odds ratio: 0.78; P=0.673). Optimized CTA-specific thresholds for plaque burden (area under the curve: 0.832 versus 0.676) and degree of stenosis (area under the curve: 0.826 versus 0.721) showed significantly higher diagnostic performance for ACS as compared with IVUS-based thresholds (all P<0.05) with borderline significance for minimal luminal area (area under the curve: 0.817 versus 0.742; P=0.066). Conclusions CTA-specific definitions of HRP features may improve the discrimination of patients with ACS as compared with IVUS-based definitions. CLINICAL TRIAL REGISTRATION URL: https://www.clinicaltrials.gov . Unique identifier: NCT01084239.

Changes in Coronary Plaque Volume: Comparison of Serial Measurements on Intravascular Ultrasound and Coronary Computed Tomographic Angiography.

Serial measurements of coronary plaque volume have been used to evaluate drug efficacy in atherosclerotic progression. However, the usefulness of computed tomography for this purpose is unknown. We investigated whether the change in total plaque volume on coronary computed tomographic angiography is associated with the change in segment plaque volume on intravascular ultrasound. We prospectively enrolled 11 consecutive patients (mean age, 56.3 ± 5 yr; 6 men) who were to undergo serial invasive coronary angiographic examinations with use of grayscale intravascular ultrasound and coronary computed tomography, performed <180 days apart at baseline and from 1 to 2 years later. Subjects underwent 186 serial measurements of total plaque volume on coronary computed tomography and 22 of segmental plaque volume on intravascular ultrasound. We used semiautomated software to examine percentage relationships and changes between total plaque and segmental plaque volumes. No significant correlations were found between percentages of total coronary and segment coronary plaque volume, nor between normalized coronary plaque volume. However, in the per-patient analysis, there were strong correlations between the imaging methods for changes in total coronary and segment coronary plaque volume (r=0.62; P=0.04), as well as normalized plaque volume (r=0.82; P=0.002). Per-patient change in plaque volume on coronary computed tomography is significantly associated with that on intravascular ultrasound. Computed tomographic angiography may be safer and more widely available than intravascular ultrasound for evaluating atherosclerotic progression in coronary arteries. Larger studies are warranted.

Quantitative coronary plaque analysis predicts high-risk plaque morphology on coronary computed tomography angiography: results from the ROMICAT II trial.

Semi-automated software can provide quantitative assessment of atherosclerotic plaques on coronary CT angiography (CTA). The relationship between established qualitative high-risk plaque features and quantitative plaque measurements has not been studied. We analyzed the association between quantitative plaque measurements and qualitative high-risk plaque features on coronary CTA. We included 260 patients with plaque who underwent coronary CTA in the Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography (ROMICAT) II trial. Quantitative plaque assessment and qualitative plaque characterization were performed on a per coronary segment basis. Quantitative coronary plaque measurements included plaque volume, plaque burden, remodeling index, and diameter stenosis. In qualitative analysis, high-risk plaque was present if positive remodeling, low CT attenuation plaque, napkin-ring sign or spotty calcium were detected. Univariable and multivariable logistic regression analyses were performed to assess the association between quantitative and qualitative high-risk plaque assessment. Among 888 segments with coronary plaque, high-risk plaque was present in 391 (44.0%) segments by qualitative analysis. In quantitative analysis, segments with high-risk plaque had higher total plaque volume, low CT attenuation plaque volume, plaque burden and remodeling index. Quantitatively assessed low CT attenuation plaque volume (odds ratio 1.12 per 1 mm3, 95% CI 1.04-1.21), positive remodeling (odds ratio 1.25 per 0.1, 95% CI 1.10-1.41) and plaque burden (odds ratio 1.53 per 0.1, 95% CI 1.08-2.16) were associated with high-risk plaque. Quantitative coronary plaque characteristics (low CT attenuation plaque volume, positive remodeling and plaque burden) measured by semi-automated software correlated with qualitative assessment of high-risk plaque features.

Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting.

PURPOSE: The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. METHODS: The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. RESULTS: For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. CONCLUSIONS: The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers.

Quantification of aortic annulus in computed tomography angiography: Validation of a fully automatic methodology.

BACKGROUND: Automatic accurate measuring of the aortic annulus and determination of the optimal angulation of X-ray projection are important for the trans-catheter aortic valve replacement (TAVR) procedure. The objective of this study was to present a novel fully automatic methodology for the quantification of the aortic annulus in computed tomography angiography (CTA) images. METHODS: CTA datasets of 26 patients were analyzed retrospectively with the proposed methodology, which consists of a knowledge-based segmentation of the aortic root and detection of the orientation and size of the aortic annulus. The accuracy of the methodology was determined by comparing the automatically derived results with the reference standard obtained by semi-automatic delineation of the aortic root and manual definition of the annulus plane. RESULTS: The difference between the automatic annulus diameter and the reference standard by observer 1 was 0.2±1.0mm, with an inter-observer variability of 1.2±0.6mm. The Pearson correlation coefficient for the diameter was good (0.92 for observer 1). For the first time, a fully automatic tool to assess the optimal projection curves was presented and validated. The mean difference between the optimal projection curves calculated based on the automatically defined annulus plane and the reference standard was 6.4° in the cranial/caudal (CRA/CAU) direction. The mean computation time was short with around 60s per dataset. CONCLUSION: The new fully automatic and fast methodology described in this manuscript not only provided precise measurements about the aortic annulus size with results comparable to experienced observers, but also predicted optimal X-ray projection curves from CTA images.

The effect of coronary bifurcation and haemodynamics in prediction of atherosclerotic plaque development: a serial computed tomographic coronary angiographic study.

AIMS: The aim of this study was to examine the effect of the daughter branches on the haemodynamics and the potential prediction of atherosclerotic plaque development as well as the best flow division model for accurate blood flow modelling. METHODS AND RESULTS: We analysed computed tomography coronary angiography retrospective data portraying 17 coronary artery bifurcations in 15 patients recruited into the PROSPECT MSCT study. Baseline and three-year follow-up imaging data were used to reconstruct coronary artery anatomy. In the baseline models blood flow simulations were performed using three flow division approaches: stress-free, Murray's law and Doriot's fit. Blood flow simulation was also performed omitting the daughter branch. The association between ESS estimated in models that incorporated the daughter branches and lumen reduction was higher than the cases where the side branch was omitted. Murray's law provides the most accurate results when comparing the different flow division models. More specifically, low ESS is a predictor of significant lumen reduction (p=0.007), plaque burden increase (p=0.0006) and necrotic core change (p=0.025). CONCLUSIONS: The ESS distribution in coronary models including the daughter branches and based on the calculations implementing Murray's law allows more accurate prediction of atherosclerotic evolution than ESS estimated in models including only the main vessel.

Automatic identification of coronary tree anatomy in coronary computed tomography angiography.

An automatic coronary artery tree labeling algorithm is described to identify the anatomical segments of the extracted centerlines from coronary computed tomography angiography (CCTA) images. This method will facilitate the automatic lesion reporting and risk stratification of cardiovascular disease. Three-dimensional (3D) models for both right dominant (RD) and left dominant (LD) coronary circulations were built. All labels in the model were matched with their possible candidates in the extracted tree to find the optimal labeling result. In total, 83 CCTA datasets with 1149 segments were included in the testing of the algorithm. The results of the automatic labeling were compared with those by two experts. In all cases, the proximal parts of main branches including LM were labeled correctly. The automatic labeling algorithm was able to identify and assign labels to 89.2% RD and 83.6% LD coronary tree segments in comparison with the agreements of the two experts (97.6% RD, 87.6% LD). The average precision of start and end points of segments was 92.0% for RD and 90.7% for LD in comparison with the manual identification by two experts while average differences in experts is 1.0% in RD and 2.2% in LD cases. All cases got similar clinical risk scores as the two experts. The presented fully automatic labeling algorithm can identify and assign labels to the extracted coronary centerlines for both RD and LD circulations.

Prediction of atherosclerotic disease progression using LDL transport modelling: a serial computed tomographic coronary angiographic study.

AIM: To investigate the efficacy of low-density lipoprotein (LDL) transport simulation in reconstructed arteries derived from computed tomography coronary angiography (CTCA) to predict coronary segments that are prone to progress. METHODS AND RESULTS: Thirty-two patients admitted with an acute coronary event who underwent 64-slice CTCA after percutaneous coronary intervention and at 3-year follow-up were included in the analysis. The CTCA data were used to reconstruct the coronary anatomy of the untreated vessels at baseline and follow-up, and LDL transport simulation was performed in the baseline models. The computed endothelial shear stress (ESS), LDL concentration, and CTCA-derived plaque characteristics were used to identify predictors of substantial disease progression (defined as an increase in the plaque burden at follow-up higher than two standard deviations of the intra-observer variability of the expert who performed the analysis). Fifty-eight vessels were analysed. High LDL concentration [odds ratio (OR): 2.16; 95% confidence interval (CI): 1.64-2.84; P = 0.0054], plaque burden (OR: 1.40; 95% CI: 1.13-1.72; P = 0.0017), and plaque area (OR: 3.46; 95% CI: 2.20-5.44; P≤ 0.0001) were independent predictors of a substantial disease progression at follow-up. The ESS appears as a predictor of disease progression in univariate analysis but was not an independent predictor when the LDL concentration was entered into the multivariate model. The accuracy of the model that included the LDL concentration was higher than the accuracy of the model that included the ESS (65.1 vs. 62.5%). CONCLUSIONS: LDL transport modelling appears a better predictor of atherosclerotic disease progression than the ESS, and combined with the atheroma characteristics provided by CTCA is able to detect with a moderate accuracy segments that will exhibit a significant plaque burden increase at mid-term follow-up.