Rahil Shahzad

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/.

Vessel Specific Coronary Artery Calcium Scoring: An Automatic System

RATIONALE AND OBJECTIVES The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images. MATERIALS AND METHODS Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.5-mm and 3.0-mm slice spacing reconstructions and were acquired using four different scanners. The scores of manual observers, who annotated the data using commercially available software, served as ground truth. An automatic method for detecting and quantifying calcifications for each of the four main coronary arteries and the whole heart was trained on 209 data sets and tested on 157 data sets. Statistical testing included determining Pearsons correlation coefficients and Bland-Altman analysis to compare performance between the system and ground truth. Wilcoxons signed-rank test was used to compare the interobserver variability to the systems performance. RESULTS Automatic detection of calcified objects was achieved with sensitivity of 81.2% per calcified object in the 1.5-mm data set and sensitivity of 86.6% per calcified object in the 3.0-mm data set. The system made an average of 2.5 errors per patient in the 1.5-mm data set and 2.2 errors in the 3.0-mm data set. Pearsons correlation coefficients of 0.97 (P < .001) for both 1.5-mm and 3.0-mm scans with respect to the calcium volume score of the whole heart were found. The average R values over Agatston, mass, and volume scores for each of the arteries (left circumflex coronary artery, right coronary artery, and left main and left anterior descending coronary arteries) were 0.93, 0.96, and 0.99, respectively, for the 1.5-mm scans. Similarly, for 3.0-mm scans, R values were 0.94, 0.94, and 0.99, respectively. Risk category assignment was correct in 95% and 89% of the data sets in the 1.5-mm and 3-mm scans. CONCLUSIONS An automatic vessel-specific coronary artery calcium scoring system was developed, and its feasibility for calcium scoring in individual vessels and risk category classification has been demonstrated.

Automatic quantification of epicardial fat volume on non‐enhanced cardiac CT scans using a multi‐atlas segmentation approach

PURPOSE There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans. METHODS Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability. RESULTS Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearsons correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearsons correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume. CONCLUSIONS The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

Epicardial fat volume is related to atherosclerotic calcification in multiple vessel beds

AIM To investigate relationships between epicardial fat volume and atherosclerosis in multiple major vessel beds. METHODS AND RESULTS From the population-based Rotterdam Study, 2298 participants underwent computed tomography examinations to quantify epicardial fat volume and atherosclerotic calcification volume in the coronary arteries, aortic arch, and extracranial and intracranial internal carotid arteries. Using linear regression modelling, we investigated relationships of epicardial fat volume with atherosclerotic calcification volume in each vessel bed, adjusting for conventional cardiovascular risk factors (waist circumference, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol, smoking, diabetes, and usage of blood pressure-lowering and lipid-lowering medication). To test whether associations of epicardial fat with calcification per vessel bed were independent of calcification elsewhere, we created a model in which all vessel beds were entered together. We found that a larger epicardial fat volume was associated with larger calcification volumes in the coronary arteries, aortic arch, and extracranial carotid arteries in both sexes. After adjustment for cardiovascular risk factors, larger epicardial fat volume was related to coronary and extracranial carotid artery calcification volume in males only [difference in calcification volume per SD increase in epicardial fat volume: 0.12 (95% confidence interval, CI: 0.04; 0.19) and 0.14 (95% CI: 0.06; 0.22)]. These associations remained unchanged after entering all vessel beds into one model. CONCLUSION Larger volumes of epicardial fat are associated with larger amounts of coronary and extracranial carotid artery atherosclerosis in males, independent of cardiovascular risk factors. This could imply that epicardial fat also exerts a systemic effect on atherosclerosis development. Future longitudinal research is warranted to further disentangle these relationships with a specific focus on sex differences.

Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium‐enhanced MRI: Towards objective atrial scar assessment

To realize objective atrial scar assessment, this study aimed to develop a fully automatic method to segment the left atrium (LA) and pulmonary veins (PV) from late gadolinium‐enhanced (LGE) magnetic resonance imaging (MRI). The extent and distribution of atrial scar, visualized by LGE‐MRI, provides important information for clinical treatment of atrial fibrillation (AF) patients.

An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework

PURPOSE The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. METHODS Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. RESULTS Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohens kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. CONCLUSIONS A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.

A patient-specific coronary density estimate

A reliable density estimate for the position of the coronary arteries in Computed Tomography (CT) data is beneficial for many coronary image processing applications, such as vessel tracking, lumen segmentations, and calcium scoring. This paper presents a method for obtaining an estimate of the coronary artery location in CT and CT angiography (CTA). The proposed method constructs a patient-specific coronary density estimate using CTA atlas registration. The method is evaluated by quantifying the overlap of the obtained density estimate with 24 manually annotated centrelines of the lumen. Furthermore, the method is quantitatively evaluated when applied in automatic calcium scoring of the coronary arteries, which is an important risk predictor of coronary artery disease. The obtained results were compared to manual annotations for 170 CT datasets.

Additional Diagnostic Value of Integrated Analysis of Cardiac CTA and SPECT MPI Using the SMARTVis System in Patients with Suspected Coronary Artery Disease

CT angiography (CTA) and SPECT myocardial perfusion imaging (MPI) are complementary imaging techniques to assess coronary artery disease (CAD). Spatial integration and combined visualization of SPECT MPI and CTA data may facilitate correlation of myocardial perfusion defects and subtending coronary arteries and thus offer additional diagnostic value over either stand-alone or side-by-side interpretation of the respective datasets from the 2 modalities. In this study, we investigated the additional diagnostic value of a software-based CTA/SPECT MPI image fusion system over conventional side-by-side analysis in patients with suspected CAD. Methods: Seventeen symptomatic patients who underwent both CTA and SPECT MPI within a 90-d period were included in our study; 7 of them also underwent invasive coronary angiography (ICA). The potential benefits of the synchronized multimodal heart visualization (SMARTVis) system in assessing CAD were investigated through a case study involving 4 experts from 2 medical centers, in which we performed, first, a side-by-side analysis using structured CTA and SPECT reports and, second, an integrated analysis using the SMARTVis system in addition to the reports. Results: The fused interpretation led to a more accurate diagnosis, reflected in an increase in the individual observers’ sensitivity and specificity to correctly refer for invasive angiography eventually followed by revascularization. For the first, second, third, and fourth observers, the respective sensitivities improved from 50%, 60%, 80%, and 80% to 70%, 80%, 100%, and 90% and the respective specificities from 100%, 94%, 83%, and 83% to 100%, 100%, 94%, and 83%. Additionally, the interobserver diagnosis agreement increased from 74% to 84%. The improvement was primarily found in patients presenting with CAD in more vessels than the number of reported perfusion defects. Conclusion: Integrated analysis of cardiac CTA and SPECT MPI using the SMARTVis system results in an improved diagnostic performance.

Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior

PURPOSE The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images. METHODS The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion. RESULTS The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification. CONCLUSIONS With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.

Epicardial Fat Volume and the Risk of Atrial Fibrillation in the General Population Free of Cardiovascular Disease

During the past years, several clinical studies demonstrated associations of the amount of epicardial fat (i.e., adipose tissue surrounding the myocardium) with the burden of atrial fibrillation (AF) and outcomes after AF ablation [(1)][1]. However, as was recently pointed out in major reviews on