Automated detection of calcified plaque using higher-order spectra cumulant technique in computer tomography angiography images

Cardiovascular disease continues to be the leading cause of death globally. Often, it stems from atherosclerosis, which can trigger substantial variations in the coronary arteries, possibly causing coronary artery disease (CAD). Coronary artery calcification is known to be a strong and independent forecaster of CAD. Hence, coronary computer tomography angiography (CTA) has become a fundamental noninvasive imaging tool to characterize coronary artery plaques. In this article, an automated algorithm is presented to uncover the presence of a calcified plaque, using 2060 CTA images acquired from 60 patients. Higher-order spectra cumulants were extracted from each image, thereby providing 2448 descriptive features per image. The features were then reduced using numerous well-established techniques, and ranked according to t value. Subsequently, the reduced features were input to several classifiers to achieve the best diagnostic accuracy with a minimum number of features. Optimal results were obtained using the support vector machine with a radial basis function, having 22 features obtained with the multiple factor analysis feature reduction algorithm. The accuracy, positive predictive value, sensitivity, and specificity obtained were 95.83%, 97.05%, 94.54%, and 97.13%, respectively. Based on these results, the technique could be useful to automatically and accurately identify calcified plaque evident in CTA images, and may therefore become an important tool to help reduce procedural costs and patient radiation dose

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening

Development of an automated measure of iliac artery tortuosity that successfully predicts early graft-related complications associated with endovascular aneurysm repair

Abstract not availableN. Dowson, M. Boult, P. Cowled, T. De Loryn, R. Fitridg

직접 볼륨 렌더링의 전이 함수 설계에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 신영길.Although direct volume rendering (DVR) has become a commodity, the design of transfer functions still a challenge. Transfer functions which map data values to optical properties (i.e., colors and opacities) highlight features of interests as well as hide unimportant regions, dramatically impacting on the quality of the visualization. Therefore, for the effective rendering of interesting features, the design of transfer functions is very important and challenging task. Furthermore, manipulation of these transfer functions is tedious and time-consuming task. In this paper, we propose a 3D spatial field for accurately identifying and visually distinguishing interesting features as well as a mechanism for data exploration using multi-dimensional transfer function. First, we introduce a 3D spatial field for the effective visualization of constricted tubular structures, called as a stenosis map which stores the degree of constriction at each voxel. Constrictions within tubular structures are quantified by using newly proposed measures (i.e., line similarity measure and constriction measure) based on the localized structure analysis, and classified with a proposed transfer function mapping the degree of constriction to color and opacity. We show the application results of our method to the visualization of coronary artery stenoses. We present performance evaluations using twenty-eight clinical datasets, demonstrating high accuracy and efficacy of our proposed method. Second, we propose a new multi-dimensional transfer function which incorporates texture features calculated from statistically homogeneous regions. This approach employs parallel coordinates to provide an intuitive interface for exploring a new multi-dimensional transfer function space. Three specific ways to use a new transfer function based on parallel coordinates enables the effective exploration of large and complex datasets. We present a mechanism for data exploration with a new transfer function space, demonstrating the practical efficacy of our proposed method. Through a study on transfer function design for DVR, we propose two useful approaches. First method to saliently visualize the constrictions within tubular structures and interactively adjust the visual appearance of the constrictions delivers a substantial aid in radiologic practice. Furthermore, second method to classify objects with our intuitive interface utilizing parallel coordinates proves to be a powerful tool for complex data exploration.Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Volume rendering 1 1.1.2 Computer-aided diagnosis 3 1.1.3 Parallel coordinates 5 1.2 Problem statement 8 1.3 Main contribution 12 1.4 Organization of dissertation 16 Chapter 2 Related Work 17 2.1 Transfer function 17 2.1.1 Transfer functions based on spatial characteristics 17 2.1.2 Opacity modulation techniques 20 2.1.3 Multi-dimensional transfer functions 22 2.1.4 Manipulation mechanism for transfer functions 25 2.2 Coronary artery stenosis 28 2.3 Parallel coordinates 32 Chapter 3 Volume Visualization of Constricted Tubular Structures 36 3.1 Overview 36 3.2 Localized structure analysis 37 3.3 Stenosis map 39 3.3.1 Overview 39 3.3.2 Detection of tubular structures 40 3.3.3 Stenosis map computation 49 3.4 Stenosis-based classification 52 3.4.1 Overview 52 3.4.2 Constriction-encoded volume rendering 52 3.4.3 Opacity modulation based on constriction 54 3.5 GPU implementation 57 3.6 Experimental results 59 3.6.1 Clinical data preparation 59 3.6.2 Qualitative evaluation 60 3.6.3 Quantitative evaluation 63 3.6.4 Comparison with previous methods 66 3.6.5 Parameter study 69 Chapter 4 Interactive Multi-Dimensional Transfer Function Using Adaptive Block Based Feature Analysis 73 4.1 Overview 73 4.2 Extraction of statistical features 74 4.3 Extraction of texture features 78 4.4 Multi-dimensional transfer function design using parallel coordinates 81 4.5 Experimental results 86 Chapter 5 Conclusion 90 Bibliography 92 초 록 107Docto

Localizing Calcifications in Cardiac CT Data Sets Using a New Vessel Segmentation Approach

The new generation of multislice computed tomography (CT) scanners allows for the acquisition of high-resolution images of the heart. Based on that image data, the heart can be analyzed in a noninvasive way—improving the diagnosis of cardiovascular malfunctions on one hand, and the planning of an eventually necessary intervention on the other. One important parameter for the evaluation of the severeness of a coronary artery disease is the number and localization of calcifications (hard plaques). This work presents a method for localizing these calcifications by employing a newly developed vessel segmentation approach. This extraction technique has been developed for, and tested with, contrast-enhanced CT data sets of the heart. The algorithm provides enough information to compute the vessel diameter along the extracted segment. An approach for automatically detecting calcified regions that combines diameter information and gray value analysis is presented. In addition, specially adapted methods for the visualization of these analysis results are described

Zeitabhängige, multimodale Modellierung und Analyse von Herzdaten

Kardiovaskuläre Erkrankungen stellen in den westlichen Industrienationen eine der Haupttodesursachen dar. Für die Diagnostik steht inzwischen mit der Computer-Tomographie ein leistungsfähiges bildgebendes Verfahren zur Verfügung. Im Rahmen dieser Arbeit wurden Verfahren entwickelt, um dem Radiologen durch eine weitgehend automatische und umfassende Analyse von 4D-CTA-Daten und der automatischen Berechnung wichtiger diagnostischer Parameter zu unterstützen

Systematic review of the clinical effectiveness and cost-effectiveness of 64-slice or higher computed tomography angiography as an alternative to invasive coronary angiography in the investigation of coronary artery disease

Objectives: To assess the clinical effectiveness and cost-effectiveness, in different patient groups, of the use of 64-slice or higher computed tomography (CT) angiography, instead of invasive coronary angiography (CA), for diagnosing people with suspected coronary artery disease (CAD) and assessing people with known CAD. Data sources: Electronic databases were searched from 2002 to December 2006. Review methods: Included studies were tabulated and sensitivity, specificity, positive and negative predictive values calculated. Meta-analysis models were fitted using hierarchical summary receiver operating characteristic curves. Summary sensitivity, specificity, positive and negative likelihood ratios and diagnostic odds ratios for each model were reported as a median and 95% credible interval (CrI). Searches were also carried out for studies on the cost-effectiveness of 64-slice CT in the assessment of CAD. Results: The diagnostic accuracy and prognostic studies enrolled over 2500 and 1700 people, respectively. The overall quality of the studies was reasonably good. In the pooled estimates, 64-slice CT angiography was highly sensitive (99%, 95% CrI 97 to 99%) for patientbased detection of significant CAD (defined as 50% or more stenosis), while across studies the negative predictive value (NPV) was very high (median 100%, range 86 to 100%). In segment-level analysis compared with patient-based detection, sensitivity was lower (90%, 95% CrI 85 to 94%, versus 99%, 95% CrI 97 to 99%) and specificity higher (97%, 95% CrI 95 to 98%, versus 89%, 95% CrI 83 to 94%), while across studies the median NPV was similar (99%, range 95 to 100%, versus 100%, range 86 to 100%). At individual coronary artery level the pooled estimates for sensitivity ranged from 85% for the left circumflex (LCX) artery to 95% for the left main artery, specificity ranged from 96% for both the left anterior descending (LAD) artery and LCX to 100% for the left main artery, while across studies the positive predictive value (PPV) ranged from 81% for the LCX to 100% for the left main artery and NPV was very high, ranging from 98% for the LAD (range 95 to 100%), LCX (range 93 to 100%) and right coronary artery (RCA) (range 94 to 100%) to 100% for the left main artery. The pooled estimates for bypass graft analysis were 99% (95% CrI 95 to 100%) sensitivity, 96% (95% CrI 86 to 99%) specificity, with median PPV and NPV values across studies of 93% (range 90 to 95%) and 99% (range 98 to 100%), respectively. This compares with, for stent analysis, a pooled sensitivity of 89% (95% CrI 68 to 97%), specificity 94% (95% CrI 83 to 98%), and median PPV and NPV values across studies of 77% (range 33 to 100%) and 96% (range 71 to 100%), respectively. Sixty-four-slice CT is almost as good as invasive CA in terms of detecting true positives. However, it is somewhat poorer in its rate of false positives. It seems likely that diagnostic strategies involving 64-slice CT will still require invasive CA for CT test positives, partly to identify CT false positives, but also because CA provides other information that CT currently does not, notably details of insertion site and distal run-off for possible coronary artery bypass graft (CABG). The high sensitivity of 64-slice CT avoids the costs of unnecessary CA in those referred for investigation but who do not have CAD. Given the possible, although small, associated death rate, avoiding these unnecessary CAs through the use of 64-slice CT may also confer a small immediate survival advantage. This in itself may be sufficient to outweigh the very marginally inferior rates of detection of true positives by strategies involving 64-slice CT. The avoidance of unnecessary CA through the use of 64-slice CT also appears likely to result in overall cost savings in the diagnostic pathway. Only if both the cost of CA is relatively low and the prevalence of CAD in the presenting population is relatively high (so that most patients will go on to CA) will the use of 64-slice CT be likely to result in a higher overall diagnostic cost per patient. Conclusions: The main value of 64-slice CT may at present be to rule out significant CAD. It is unlikely to replace CA in assessment for revascularisation of patients, particularly as angiography and angioplasty are often done on the same occasion. Further research is needed into the marginal advantages and costs of 256-slice machines compared with 64-sliceCT, the usefulness of 64-slice CT in people with suspected acute coronary syndrome, the potential of multislice computed tomography to examine plaque morphology, the role of CT in identifying patients suitable for CABG, and the concerns raised about repetitive use, or use of 64-slice or higher CT angiography in younger individuals or women of childbearing age.The Health Services Research Unit, Institute of Applied Health Sciences, University of Aberdeen, is core-funded by the Chief Scientist Office of the Scottish Government Health Directorates